diff --git a/examples/causal_inference/bayesian_nonparametric_causal.ipynb b/examples/causal_inference/bayesian_nonparametric_causal.ipynb
index 81739e965..752813093 100644
--- a/examples/causal_inference/bayesian_nonparametric_causal.ipynb
+++ b/examples/causal_inference/bayesian_nonparametric_causal.ipynb
@@ -27,7 +27,8 @@
     "import pandas as pd\n",
     "import pymc as pm\n",
     "import pymc_bart as pmb\n",
-    "import pytensor.tensor as pt"
+    "import pytensor.tensor as pt\n",
+    "import statsmodels.api as sm"
    ]
   },
   {
@@ -49,9 +50,9 @@
     "\n",
     "You always run the risk of being wrong. \n",
     "\n",
-    "Your appetite for that risk is likely proportional to the strength of the claim you are making - less concerned with _casual_ assertions than __causal__ claims! There are few claims stronger than the assertion of a causal relationship. Each such statement is an inferential gamble underwritten by our faith in (occasionally arcance) methodology. \n",
+    "Your appetite for that risk is likely proportional to the strength of the claim you are making - less concerned with _casual_ assertions than __causal__ claims!  Each causal statement is an inferential gamble underwritten by our faith in (occasionally arcane) methodology. The conversational stakes are high - you risk your brittle credibility with false claims. There are few claims stronger than the assertion of a causal relationship, few claims more contestable. A naive world model, rich with tenuous connections and non-sequiter implications will expose you as an idiotic charlatan overly impressed by conspiracy theory. We don't that for you. \n",
     "\n",
-    "In this notebook we will explain and motivate the usage of propensity scores in analysis of causal inference questions. We will avoid the impression of magic - our focus will be on the manner in which we (a) estimate propensity scores and (b) use them in the analysis of causal questions. We will see how they help avoid risks of selection bias in causal inference.\n",
+    "In this notebook we will explain and motivate the usage of propensity scores in analysis of causal inference questions. We will avoid the impression of magic - our focus will be on the manner in which we (a) estimate propensity scores and (b) use them in the analysis of causal questions. We will see how they help avoid risks of selection bias in causal inference. This method should be comfortable for the Bayeisan analyst who is familiar with weighting and re-weighting their claims with with information in the form priors. Propensity score weighting is just another opportunity to enrich your model with knowledge about the world.\n",
     "\n",
     "We will illustrate these patterns using two data sets: (i) the NHEFS data used througout Miguel Hernan's _Causal Inference: What If_ book and a second patient focused data set used throughout _Bayesian Nonparametrics for Causal Inference and Missing Data_ by Daniels, Linero and Roy. Throughout we will contrast the use of non-parametric BART models with simpler regression models for the estimation of propensity scores and causal effects."
    ]
@@ -1355,7 +1356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 709,
+   "execution_count": 784,
    "metadata": {},
    "outputs": [
     {
@@ -1532,7 +1533,7 @@
        "4   1600  7584.6681               400         361  "
       ]
      },
-     "execution_count": 709,
+     "execution_count": 784,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1560,7 +1561,7 @@
     "\n",
     "We specify two types of model which are to  be assessed. One which relies entirely on the Logistic regression and another which uses BART to model the relationships between and the covariates and the outcome. The BART model has the benefit of using a tree-based algorithm to explore the interaction effects among the various strata in our sample data. \n",
     "\n",
-    "Having a flexible model like BART is key to understanding what we are doing when we undertake inverse propensity weighting (IVPw) adjustments. The thought is that any given strata in our dataset will be described by a set of covariates. Types of individual will be represented by these covariate profiles. The share of observations within our data which are picked out by any given covariate profile represents a bias towards that type of individual. If our treatment status is such that individuals will more or less actively select themselves into the status, then a naive comparisons of differences between treatment groups and control groups will be misleading to the degree that we have over-represented types of individual (covariate profiles) in the population.\n",
+    "Having a flexible model like BART is key to understanding what we are doing when we undertake inverse propensity weighting (IVPw) adjustments. The thought is that any given strata in our dataset will be described by a set of covariates. Types of individual will be represented by these covariate profiles - the attribute vector $X$. The share of observations within our data which are picked out by any given covariate profile represents a bias towards that type of individual. If our treatment status is such that individuals will more or less actively select themselves into the status, then a naive comparisons of differences between treatment groups and control groups will be misleading to the degree that we have over-represented types of individual (covariate profiles) in the population.\n",
     "\n",
     "Randomisation solves this. But we can't always randomise.\n",
     "\n",
@@ -1877,7 +1878,7 @@
     "\n",
     "Firstly, and somewhat superficially, the propensity score is a dimension reduction technique. We take a complex covariate profile $X_{i}$ and reduce it to a scaler $p^{i}_{T}(X)$. It is tool for thinking about the potential outcomes of an individual under different treatment regimes. In a policy evaluation context it can help partial out the degree of incentives for policy adoption strata of the population. \n",
     "\n",
-    "The pivotal idea is that we cannot license causal claims unless (i) the treatment assignment is independent of the individual's covariate profile $T     \\perp\\!\\!\\!\\perp X$  and (ii) the outcomes $Y(0)$, and $Y(1)$ and similarly conditionally independent of the treatement $T | X$. If these conditions hold, then we say that $T$ is __strongly ignorable__ under $X$. It is a theorem that if $T$ is strongly ignorable under $X$, then (ii) holds under $p_{T}(X)$. So valid statistical inference proceeds in a lower dimensional space using the propensity score as a proxy for the higher dimensional data. There is a great discussion of the details in Aronow and Miller's _Foundations of Agnostic Statistics_.\n",
+    "The pivotal idea is that we cannot license causal claims unless (i) the treatment assignment is independent of the covariate profiles i.e $T     \\perp\\!\\!\\!\\perp X$  and (ii) the outcomes $Y(0)$, and $Y(1)$ and similarly conditionally independent of the treatement $T | X$. If these conditions hold, then we say that $T$ is __strongly ignorable__ given $X$. It is a theorem that if $T$ is strongly ignorable given $X$, then (ii) holds given $p_{T}(X)$. So valid statistical inference proceeds in a lower dimensional space using the propensity score as a proxy for the higher dimensional data. There is a great discussion of the details in Aronow and Miller's _Foundations of Agnostic Statistics_.\n",
     "\n",
     "We are, as with all causal inference methods, making the assumption of __strong ignorability__. But given this assumption that we are measuring the right covariate profile to induce strong ignorability, then propensity scores can be used thoughtfully to underwrite causal claims. With observational data we cannot re-run the assignment mechanism but we can estimate it, and transform our data to proportionally weight the data summaries within each group so that the analysis is less effected by the over-representation of different strata in each group. This is what we hope to use the propensity scores to achieve. \n",
     "\n"
@@ -2739,6 +2740,19 @@
     "These weighting schemes can now be incorporated into various models of statistical summaries so as to \"correct\" the reprensentation of covariate profiles across both groups. If an individual's propensity score is such that they are are highly likely to receive the treatment status e.g .95 then we want to downweight their importance if they occur in the treatment and upweight their importance if they appear in the control group. This makes sense because their high propensity score implies that similar individuals are already heavily present in the treatment group, but less likely to occur in the control group. Hence our corrective strategy re-weights their contribution to the summary statistics across each group. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Robust and Doubly Robust Propensity Scores\n",
+    "\n",
+    "We've been keen to stress that IVPw are a corrective. An opportunity for the causal analyst to put their finger on the scale and adjust the representative shares accorded to individuals in the treatment and control groups. As such there are no universal correctives, and naturally a variety of alternatives have arisen to fill gaps where simple propensity score weighting fails. We will see below a number of alternative weighting schemes. \n",
+    "\n",
+    "The main distinction to call out is between the raw propensity score weights and the doubly-robust theory of propensity score weights. \n",
+    "\n",
+    "Doubly robust methods are so named as they represent a compromise estimtator for causal effect that combines (i) a treatment assignment model (like propensity scores) and (ii) a more direct response outcome model. The method combines these two estimators in a way to generate a statistically unbiased estimate of the treatment effect. They work well because the way they are combined requires that only one of the models needs to be well-specified. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2746,31 +2760,14 @@
     "## Estimated Expected Causal Effect (ATE)\n",
     "\n",
     "The next code block builds a set of functions to pull out an extract a sample from our posterior distribution of propensity scores and use this propensity score to reweight the observed outcome variable across our treatment and control groups to re-calculate the average treatment effect (ATE). It reweights our data using the inverse probability weighting scheme and then plots three views (1) the raw propensity scores across groups (2) the raw outcome distribution and (3) the re-weighted outcome distribution. \n",
-    "\n",
-    "### The Logit Propensity Model"
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 675,
+   "execution_count": 786,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1500 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 1511,
-       "width": 1511
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "def plot_weights(bins, top0, top1, ylim, ax):\n",
     "    ax.axhline(0, c=\"gray\", linewidth=1)\n",
@@ -2803,7 +2800,7 @@
     "    ],\n",
     "    text_pos=(20, 80),\n",
     "    ps=None,\n",
-    "    robust=True,\n",
+    "    method=\"robust\",\n",
     "):\n",
     "    X = X.copy()\n",
     "    if ps is None:\n",
@@ -2814,20 +2811,32 @@
     "    else:\n",
     "        X[\"ps\"] = ps\n",
     "    X[\"trt\"] = t\n",
-    "    if robust:\n",
+    "    propensity0 = X[X[\"trt\"] == 0][\"ps\"]\n",
+    "    propensity1 = X[X[\"trt\"] == 1][\"ps\"]\n",
+    "    if method == \"robust\":\n",
     "        p_of_t = X[\"trt\"].mean()\n",
     "        X[\"i_ps\"] = np.where(t, (p_of_t / X[\"ps\"]), (1 - p_of_t) / (1 - X[\"ps\"]))\n",
     "        n_ntrt = X[X[\"trt\"] == 0].shape[0]\n",
     "        n_trt = X[X[\"trt\"] == 1].shape[0]\n",
-    "    else:\n",
+    "    elif method == \"raw\":\n",
     "        X[\"ps\"] = np.where(X[\"trt\"], X[\"ps\"], 1 - X[\"ps\"])\n",
     "        X[\"i_ps\"] = 1 / X[\"ps\"]\n",
     "        n_ntrt = n_trt = len(X)\n",
-    "    X[\"log_y\"] = y\n",
+    "    else:\n",
+    "        m0 = sm.OLS(y[t == 0], X[t == 0]).fit()\n",
+    "        m1 = sm.OLS(y[t == 1], X[t == 1]).fit()\n",
+    "        m0_pred = m0.predict(X)\n",
+    "        m1_pred = m0.predict(X)\n",
+    "        X[\"trt\"] = t\n",
+    "        X[\"y\"] = y\n",
+    "        p_of_t = X[\"trt\"].mean()\n",
+    "        X[\"i_ps\"] = np.where(t, (p_of_t / X[\"ps\"]), (1 - p_of_t) / (1 - X[\"ps\"]))\n",
+    "\n",
+    "        weighted_outcome0 = (1 - X[\"trt\"]) * (X[\"y\"] - m0_pred) / (1 - X[\"ps\"]) + m0_pred\n",
+    "        weighted_outcome1 = X[\"trt\"] * (X[\"y\"] - m1_pred) / X[\"ps\"] + m1_pred\n",
+    "    X[\"outcome\"] = y\n",
     "\n",
     "    bins = np.arange(0.025, 0.85, 0.05)\n",
-    "    propensity0 = X[X[\"trt\"] == 0][\"ps\"]\n",
-    "    propensity1 = X[X[\"trt\"] == 1][\"ps\"]\n",
     "    top0, _ = np.histogram(propensity0, bins=bins)\n",
     "    top1, _ = np.histogram(propensity1, bins=bins)\n",
     "\n",
@@ -2846,25 +2855,43 @@
     "    )\n",
     "\n",
     "    bins = lower_bins\n",
+    "\n",
+    "    outcome_trt = X[X[\"trt\"] == 1][\"outcome\"]\n",
+    "    outcome_ntrt = X[X[\"trt\"] == 0][\"outcome\"]\n",
     "    i_propensity0 = X[X[\"trt\"] == 0][\"i_ps\"]\n",
     "    i_propensity1 = X[X[\"trt\"] == 1][\"i_ps\"]\n",
-    "    outcome_trt = X[X[\"trt\"] == 1][\"log_y\"]\n",
-    "    outcome_ntrt = X[X[\"trt\"] == 0][\"log_y\"]\n",
-    "    propensity0 = i_propensity0 * outcome_ntrt\n",
-    "    propensity1 = i_propensity1 * outcome_trt\n",
     "\n",
-    "    top0, _ = np.histogram(propensity0, bins=bins)\n",
-    "    top1, _ = np.histogram(propensity1, bins=bins)\n",
-    "    plot_weights(bins, top0, top1, ylims[1], axs[2])\n",
     "    axs[2].set_ylabel(\"No. Patients\", fontsize=14)\n",
-    "    axs[2].set_xlabel(\"Estimated IP Weighted Outcome \\n Shifted\", fontsize=14)\n",
-    "    axs[2].text(text_pos[0], text_pos[1], f\"Control: E(Y) = {propensity0.sum() / n_ntrt}\")\n",
-    "    axs[2].text(text_pos[0], text_pos[1] - 20, f\"Treatment: E(Y) = {propensity1.sum() / n_trt}\")\n",
-    "    axs[2].text(\n",
-    "        text_pos[0],\n",
-    "        text_pos[1] - 40,\n",
-    "        f\"tau: E(Y(1) - Y(0)) = {propensity0.sum() / n_ntrt - propensity1.sum() / n_trt}\",\n",
-    "    )\n",
+    "    if method in [\"raw\", \"robust\"]:\n",
+    "        weighted_outcome1 = outcome_trt * i_propensity1\n",
+    "        weighted_outcome0 = outcome_ntrt * i_propensity0\n",
+    "        top0, _ = np.histogram(weighted_outcome0, bins=bins)\n",
+    "        top1, _ = np.histogram(weighted_outcome1, bins=bins)\n",
+    "        plot_weights(bins, top0, top1, ylims[1], axs[2])\n",
+    "        axs[2].set_xlabel(\"Estimated IP Weighted Outcome \\n Shifted\", fontsize=14)\n",
+    "        axs[2].text(text_pos[0], text_pos[1], f\"Control: E(Y) = {weighted_outcome0.sum() / n_ntrt}\")\n",
+    "        axs[2].text(\n",
+    "            text_pos[0], text_pos[1] - 20, f\"Treatment: E(Y) = {weighted_outcome1.sum() / n_trt}\"\n",
+    "        )\n",
+    "        axs[2].text(\n",
+    "            text_pos[0],\n",
+    "            text_pos[1] - 40,\n",
+    "            f\"tau: E(Y(1) - Y(0)) = {weighted_outcome0.sum() / n_ntrt - weighted_outcome1.sum() / n_trt}\",\n",
+    "        )\n",
+    "    else:\n",
+    "        top0, _ = np.histogram(weighted_outcome0, bins=bins)\n",
+    "        top1, _ = np.histogram(weighted_outcome1, bins=bins)\n",
+    "        plot_weights(bins, top0, top1, ylims[1], axs[2])\n",
+    "        trt = np.mean(X[\"trt\"] * (X[\"y\"] - m1_pred) / X[\"ps\"] + m1_pred)\n",
+    "        ntrt = np.mean((1 - X[\"trt\"]) * (X[\"y\"] - m0_pred) / (1 - X[\"ps\"]) + m0_pred)\n",
+    "        axs[2].set_xlabel(\"Estimated IP Weighted Outcome \\n Shifted\", fontsize=14)\n",
+    "        axs[2].text(text_pos[0], text_pos[1], f\"Control: E(Y) = {ntrt}\")\n",
+    "        axs[2].text(text_pos[0], text_pos[1] - 20, f\"Treatment: E(Y) = {trt}\")\n",
+    "        axs[2].text(\n",
+    "            text_pos[0],\n",
+    "            text_pos[1] - 40,\n",
+    "            f\"tau: E(Y(1) - Y(0)) = {ntrt - trt}\",\n",
+    "        )\n",
     "\n",
     "    top0, _ = np.histogram(outcome_ntrt, bins=bins)\n",
     "    top1, _ = np.histogram(outcome_trt, bins=bins)\n",
@@ -2877,10 +2904,41 @@
     "        text_pos[0],\n",
     "        text_pos[1] - 40,\n",
     "        f\"tau: E(Y(1) - Y(0)) = {outcome_trt.mean() - outcome_ntrt.mean()}\",\n",
-    "    )\n",
-    "\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Logit Propensity Model\n",
     "\n",
-    "make_plot(X, idata_logit)"
+    "We plot the outcome and re-weighted outcome distribution using the robust propensity score estimation method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 787,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1500 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 1511,
+       "width": 1511
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "make_plot(X, idata_logit, method=\"robust\")"
    ]
   },
   {
@@ -2892,7 +2950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 676,
+   "execution_count": 797,
    "metadata": {},
    "outputs": [
     {
@@ -2965,49 +3023,71 @@
        "4 -3.216466  5.049095  1.832629"
       ]
      },
-     "execution_count": 676,
+     "execution_count": 797,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "def get_ate(X, t, y, i, idata, robust=False):\n",
+    "def get_ate(X, t, y, i, idata, method=\"doubly_robust\"):\n",
     "    X = X.copy()\n",
     "    X[\"ps\"] = idata[\"posterior\"][\"p\"].stack(z=(\"chain\", \"draw\"))[:, i].values\n",
-    "    X[\"trt\"] = t\n",
-    "    if robust:\n",
+    "    if method == \"robust\":\n",
+    "        X[\"trt\"] = t\n",
     "        p_of_t = X[\"trt\"].mean()\n",
     "        X[\"i_ps\"] = np.where(t, (p_of_t / X[\"ps\"]), (1 - p_of_t) / (1 - X[\"ps\"]))\n",
-    "    else:\n",
+    "        i_propensity0 = X[X[\"trt\"] == 0][\"i_ps\"]\n",
+    "        i_propensity1 = X[X[\"trt\"] == 1][\"i_ps\"]\n",
+    "    elif method == \"raw\":\n",
+    "        X[\"trt\"] = t\n",
     "        X[\"ps\"] = np.where(X[\"trt\"], X[\"ps\"], 1 - X[\"ps\"])\n",
     "        X[\"i_ps\"] = 1 / X[\"ps\"]\n",
+    "        i_propensity0 = X[X[\"trt\"] == 0][\"i_ps\"]\n",
+    "        i_propensity1 = X[X[\"trt\"] == 1][\"i_ps\"]\n",
+    "    else:\n",
+    "        m0 = sm.OLS(y[t == 0], X[t == 0]).fit()\n",
+    "        m1 = sm.OLS(y[t == 1], X[t == 1]).fit()\n",
+    "        m0_pred = m0.predict(X)\n",
+    "        m1_pred = m0.predict(X)\n",
+    "        X[\"trt\"] = t\n",
+    "        X[\"y\"] = y\n",
     "    X[\"outcome\"] = y\n",
-    "    i_propensity0 = X[X[\"trt\"] == 0][\"i_ps\"]\n",
-    "    i_propensity1 = X[X[\"trt\"] == 1][\"i_ps\"]\n",
     "    outcome_trt = X[X[\"trt\"] == 1][\"outcome\"]\n",
     "    outcome_ntrt = X[X[\"trt\"] == 0][\"outcome\"]\n",
-    "    weighted_outcome_ntrt = i_propensity0 * outcome_ntrt\n",
-    "    weighted_outcome_trt = i_propensity1 * outcome_trt\n",
-    "    if robust:\n",
+    "    if method == \"robust\":\n",
+    "        weighted_outcome_ntrt = i_propensity0 * outcome_ntrt\n",
+    "        weighted_outcome_trt = i_propensity1 * outcome_trt\n",
     "        ntrt = weighted_outcome_ntrt.sum() / len(X[X[\"trt\"] == 0])\n",
     "        trt = weighted_outcome_trt.sum() / len(X[X[\"trt\"] == 1])\n",
-    "    else:\n",
+    "    elif method == \"raw\":\n",
+    "        weighted_outcome_ntrt = i_propensity0 * outcome_ntrt\n",
+    "        weighted_outcome_trt = i_propensity1 * outcome_trt\n",
     "        ntrt = weighted_outcome_ntrt.sum() / len(X)\n",
     "        trt = weighted_outcome_trt.sum() / len(X)\n",
+    "    else:\n",
+    "        trt = np.mean(X[\"trt\"] * (X[\"y\"] - m1_pred) / X[\"ps\"] + m1_pred)\n",
+    "        ntrt = np.mean((1 - X[\"trt\"]) * (X[\"y\"] - m0_pred) / (1 - X[\"ps\"]) + m0_pred)\n",
     "    ate = ntrt - trt\n",
     "    return [ate, trt, ntrt]\n",
     "\n",
     "\n",
     "qs = range(4000)\n",
-    "ate_dist = [get_ate(X, t, y, q, idata_logit, robust=True) for q in qs]\n",
+    "ate_dist = [get_ate(X, t, y, q, idata_logit, method=\"robust\") for q in qs]\n",
     "\n",
     "ate_dist_df_logit = pd.DataFrame(ate_dist, columns=[\"ATE\", \"E(Y(1))\", \"E(Y(0))\"])\n",
     "ate_dist_df_logit.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we plot the posterior distribution of the ATE. "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 612,
+   "execution_count": 789,
    "metadata": {},
    "outputs": [
     {
@@ -3060,19 +3140,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### The BART Propensity Model\n",
+    "## The BART Propensity Model\n",
     "\n",
-    "We apply exactly the same procedures as above, but now we use the propensity distribution achieved using the BART non-parametric model."
+    "Next we'll apply the doubly robust estimator to the propensity distribution achieved using the BART non-parametric model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 514,
+   "execution_count": 795,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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AAAAAAACg3CM8DwAAAAAAAAAAAAAAAAAAAAAo9wjPAwAAAAAAAAAAAAAAAAAAAADKPcLzAAAAAAAAAAAAAAAAAAAAAIByj/A8AAAAAAAAAAAAAAAAAAAAAKDcIzwPAAAAAAAAAAAAAAAAAAAAACj3CM8DAAAAAAAAAAAAAAAAAAAAAMo9wvMAAAAAAAAAAAAAAAAAAAAAgHKP8DwAAAAAAAAAAAAAAAAAAAAAoNwjPA8AAAAAAAAAAAAAAAAAAAAAKPcIzwMAAAAAAAAAAAAAAAAAAAAAyj3C8wAAAAAAAAAAAAAAAAAAAACAco/wPAAAAAAAAAAAAAAAAAAAAACg3Pv/8dmG4A5Wva8AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1500x1500 with 3 Axes>"
       ]
@@ -3087,12 +3167,12 @@
     }
    ],
    "source": [
-    "make_plot(X, idata_probit)"
+    "make_plot(X, idata_probit, method=\"doubly_robust\", ylims=[(-150, 370), (-220, 150), (-50, 120)])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 515,
+   "execution_count": 798,
    "metadata": {},
    "outputs": [
     {
@@ -3124,33 +3204,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-3.735256</td>\n",
-       "      <td>5.527419</td>\n",
-       "      <td>1.792163</td>\n",
+       "      <td>-3.757849</td>\n",
+       "      <td>5.526190</td>\n",
+       "      <td>1.768341</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>-3.814774</td>\n",
-       "      <td>5.572124</td>\n",
-       "      <td>1.757350</td>\n",
+       "      <td>-3.713735</td>\n",
+       "      <td>5.481691</td>\n",
+       "      <td>1.767956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-3.571520</td>\n",
-       "      <td>5.302353</td>\n",
-       "      <td>1.730834</td>\n",
+       "      <td>-3.511737</td>\n",
+       "      <td>5.275200</td>\n",
+       "      <td>1.763463</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-3.833246</td>\n",
-       "      <td>5.607327</td>\n",
-       "      <td>1.774081</td>\n",
+       "      <td>-3.860172</td>\n",
+       "      <td>5.630067</td>\n",
+       "      <td>1.769896</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-3.305759</td>\n",
-       "      <td>5.109357</td>\n",
-       "      <td>1.803598</td>\n",
+       "      <td>-3.499208</td>\n",
+       "      <td>5.269444</td>\n",
+       "      <td>1.770236</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -3158,32 +3238,32 @@
       ],
       "text/plain": [
        "        ATE   E(Y(1))   E(Y(0))\n",
-       "0 -3.735256  5.527419  1.792163\n",
-       "1 -3.814774  5.572124  1.757350\n",
-       "2 -3.571520  5.302353  1.730834\n",
-       "3 -3.833246  5.607327  1.774081\n",
-       "4 -3.305759  5.109357  1.803598"
+       "0 -3.757849  5.526190  1.768341\n",
+       "1 -3.713735  5.481691  1.767956\n",
+       "2 -3.511737  5.275200  1.763463\n",
+       "3 -3.860172  5.630067  1.769896\n",
+       "4 -3.499208  5.269444  1.770236"
       ]
      },
-     "execution_count": 515,
+     "execution_count": 798,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ate_dist_probit = [get_ate(X, t, y, q, idata_probit, robust=True) for q in qs]\n",
+    "ate_dist_probit = [get_ate(X, t, y, q, idata_probit, method=\"doubly_robust\") for q in qs]\n",
     "ate_dist_df_probit = pd.DataFrame(ate_dist_probit, columns=[\"ATE\", \"E(Y(1))\", \"E(Y(0))\"])\n",
     "ate_dist_df_probit.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 613,
+   "execution_count": 799,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 2000x700 with 2 Axes>"
       ]
@@ -3207,7 +3287,9 @@
    "source": [
     "### Considerations Choosing between models\n",
     "\n",
-    "It is one thing to evalute change in average over the population, but we might want to allow for the idea of effect heterogenity across the population and as such the BART model is generally better at ensuring accurate predictions and therefore better more calibrated propensity scores acros the deepter strata of our data. "
+    "It is one thing to evalute change in average over the population, but we might want to allow for the idea of effect heterogenity across the population and as such the BART model is generally better at ensuring accurate predictions and therefore better more calibrated propensity scores acros the deepter strata of our data. But the flexibility of machine learning models for prediction tasks do not guarantee that the propensity scores attributed across the sample are well calibrated to recover the true-treatment effects when used in causal effect estimation. We have to careful in how we use the flexibility of non-parametric models in the causal context. \n",
+    "\n",
+    "First observe the hetereogenous accuracy induced by the BART model across increasingly narrow strata of our sample. "
    ]
   },
   {
@@ -3263,7 +3345,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Observations like this go along way to motivating the work in the Double ML causal inference paradigm. The model used to capture the outcome distribution or the propensity score distribution ought to be sensetive to variation across extremities of the data. We can see above that the predictive power of the simpler logistic regression model deterioriates as we progress down the partitions of the data. We will see an example below where it is key to partial out the causal effects across the quantiles of the response distribution. "
+    "Observations like this go along way to motivating the work in the Double ML causal inference paradigm. The model used to capture the outcome distribution or the propensity score distribution ought to be sensetive to variation across extremities of the data. We can see above that the predictive power of the simpler logistic regression model deterioriates as we progress down the partitions of the data. We will see an example below where this flexibility becomes a problem. "
    ]
   },
   {
@@ -3271,7 +3353,8 @@
    "metadata": {},
    "source": [
     "### Regression with Propensity Scores\n",
-    "\n"
+    "\n",
+    "Another perhaps more direct method of causal inference is to just use regression directly. Angrist and Pischke suggest that the familiar properties of regression make it more desirable, but acknowledge that there is a role for propensity and that the methods can be combined by the cautious analyst.\n"
    ]
   },
   {
@@ -3412,6 +3495,13 @@
     "model_ps_reg, idata_ps_reg = make_prop_reg_model(X, t, y, idata_logit)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fitting the regression model using the propensity as a dimensional reduction technique seems to work well here. We recover substantially the same treatment effect estimate as above. "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 714,
@@ -3759,7 +3849,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Causal Inference as Regression Imputation"
+    "### Causal Inference as Regression Imputation\n",
+    "\n",
+    "Above we read-off the causal effect estimate as the coefficient on the treatment variable in our regression model. An arguably more direct approach uses the fitted regression models to impute the distribution of potential outcomes under different treatment regimes. In this way we have yet another perspective on causal inference "
    ]
   },
   {
@@ -8226,12 +8318,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Health Expenditure Data"
+    "All perspectives on the question of causal inference here seem broadly convergent. Next we'll see an example where the choices an analyst makes can go quite wrong. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Health Expenditure Data\n",
+    "\n",
+    "We will begin with looking a health-expenditure data set analysed in _Bayesian Nonparametrics for Causal Inference and Missing Data_ . The telling feature about this data set is absence of obvious causal impact on expenditure due to the presence of smoking. We follow the authors and try and model the effect of `smoke` on the logged out `log_y`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 696,
+   "execution_count": 800,
    "metadata": {},
    "outputs": [
     {
@@ -8514,7 +8615,7 @@
        "[16430 rows x 15 columns]"
       ]
      },
-     "execution_count": 696,
+     "execution_count": 800,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8528,6 +8629,13 @@
     "df"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Some basic Summaries"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 697,
@@ -10436,6 +10544,13 @@
     "strata_expected_df"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It certaintly seems that there is little to no impact due to our treatment effect in the data. Can we recover this insight using the method of inverse propensity score weighting. But first, let's do some basic exploratory data analysis to confirm our intuition. "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 131,
@@ -10509,6 +10624,13 @@
     "axs[1].set_title(\"Empirical Cumulative \\n Densities\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plots would seem to confirm undifferentiated nature of the outcome across the two groups. With some hint of difference at the outer quantiles  of the distribution. "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 132,
@@ -10559,9 +10681,16 @@
     "axs[1].set_title(\"Differences across the Quantiles\");"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What could go Wrong?"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 679,
+   "execution_count": 801,
    "metadata": {},
    "outputs": [
     {
@@ -10923,7 +11052,7 @@
        "[1000 rows x 19 columns]"
       ]
      },
-     "execution_count": 679,
+     "execution_count": 801,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10959,7 +11088,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 479,
+   "execution_count": 803,
    "metadata": {},
    "outputs": [
     {
@@ -11003,8 +11132,8 @@
       "text/html": [
        "\n",
        "    <div>\n",
-       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [8000/8000 00:29&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "      <progress value='20000' class='' max='20000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [20000/20000 01:26&lt;00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -11019,7 +11148,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 30 seconds.\n",
+      "Sampling 4 chains for 1_000 tune and 4_000 draw iterations (4_000 + 16_000 draws total) took 87 seconds.\n",
       "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
       "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
       "Sampling: [t_pred]\n"
@@ -11052,6 +11181,113 @@
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='16000' class='' max='16000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [16000/16000 00:01&lt;00:00]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling: [b, t_pred]\n",
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [b]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      <progress value='8000' class='' max='8000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [8000/8000 00:17&lt;00:00 Sampling 4 chains, 0 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 18 seconds.\n",
+      "Sampling: [t_pred]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
@@ -11071,17 +11307,29 @@
     }
    ],
    "source": [
-    "m_ps_expend, idata_expend = make_propensity_model(X, t, bart=True, probit=True, samples=1000)"
+    "m_ps_expend_bart, idata_expend_bart = make_propensity_model(\n",
+    "    X, t, bart=True, probit=True, samples=4000\n",
+    ")\n",
+    "m_ps_expend_logit, idata_expend_logit = make_propensity_model(X, t, bart=False, samples=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Non-Parametric BART Model Propensity Model is mis-specified\n",
+    "\n",
+    "The flexibility of the BART model fit is poorly calibrated to recover the average treatment effect. Let's evaluate the weighted outcome distributions under the robust IVPw estimate. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 590,
+   "execution_count": 815,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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gruTkZAQFBSE4OBhxcXFISkoCANjY2MDKygrly5dH9erVC+Xal5SUhEePHiE4OBgJCQnQ19dHqVKlULp0aTRs2FCnK0Olpqbi/v37CAoKQnx8PPT09GBtbY2KFSuidu3aognhJGRpaQkPDw9cunRJLi6VShEUFKSzcYarqyscHBwQHR0tF799+zZ69+6tdDvR0dEIDg6Wi31ajcTd3R1v3ryRa1sVMplMtFp9kyZNVGrnc4mJiTnj3fj4eLl7ZWdnZ9SsWRN6ekW3ptiHDx/w8OFDvHnzBlKpNOf+ytXVFbVr1y7U+6uS+D3EJykpKTnnTWxsLFJTU2FlZQU7OzuULl0aderU0fl9UnBwMJ4+fYqIiAikpqbCzMwMTk5OqF69OipXrqzTY6srPT0dmzZtEn1swYIFWv2c+PHHH3H9+nUkJCTIxU+ePIlvvvkG1apV09qxSrrMzEy8fPkSwcHBOfebEokE5ubmsLW1ReXKlVGpUiUYGxtr9bhF8fuIhIQEPH78GMHBwZBKpQCQc9/t5uam8tgvLCwMz549w7t375CUlAQrKys4ODigfv36BTIprKh9t0pEREREVBwweZ6IiIiIiKiQbdiwAZ6ennKxSZMmYfLkyTn/jo2NxY4dO3Dw4EFBEsTnqlSpgqFDh2LgwIFK//BUvXr1fLfx8/PLd7vnz58rdTwACA0Nxb59+3DmzBmEhobmuW3FihXRrl07jBo1Ck5OTkof43PKPMdBQUHYsWMHTp8+jQ8fPoi2U758ebmEjjlz5uDQoUNy2yxbtgx9+/bN+ffjx4+xY8cOXLx4MecHudw8PDzg4uKS799x584dHDx4EBcvXkRcXJzC7fT09FCvXj106tQJgwcP1mpylZ+fH7Zt24Zr164prJhpZmaGDh06YOzYsUqdX8VZ7dq1ReOxsbGicV9fX8ydO1cu1qdPHyxfvjzn35GRkdi1axeOHj2qcCn63OeZmOTkZBw4cABHjhzBkydPRCtUf2JmZobWrVtj0KBBaNasWZ7t5mX48OGCKqI7d+6Ue99cvnwZf/75J27fvo2srCzRdiwsLNC9e3eMGzdOqfeGKmJiYuDt7Y1Tp07h5cuXeW5bpkwZtG7dGl9//bXaievKvOZSqRR79+6Ft7d3ntfEcuXKoX///hg5cqRKE27EJCQk4MiRIzhy5AgeP36c5/nxiZmZGWrXrg13d3d8+eWXqFGjhlLHateuHcLDw+ViFy5cEH1tw8LC0L59+zzb8/T0FFzTxTg7O+PixYsAgKysLLRr1w4RERFy26xYsUKlREJFEhIS0KpVK6SmpsrFc5//2vT+/XtBzMLCQifH0tSTJ09w8OBBXLhwQfAaiKlQoQKaN2+OL7/8Es2aNYNEIlHruNevX8f+/fvxzz//ICUlReF2EokEtWvXRq9evdC/f3+1318FeY0HCn489bmAgAAcPHgQp06dynNs+rnSpUujXr16aNWqlUYTEjWRmpqKq1ev4saNG7h16xYCAwPzvf5ZWFigYcOGGDRoENq3b6/2+Qgodz309/fHtm3b8M8//yA9PV20HWNjYzRr1gzfffcd6tatq3Z/cnv27Bm2bNmCc+fOKTy2kZERWrVqhTFjxqBRo0ZaO3ZJVbt2bUHyPPDxGq7LSXoeHh44ceKEXEzV6vBi239Knvfw8ICvr29O/MmTJ0hKSlI6Yfb58+ei9zOqfmYmJibi4MGDOH78OJ48eYLs7GyF29rZ2eGLL77A0KFDUadOHZWO8zmx+xtV7oVzO3/+PHbs2IE7d+4o7L+ZmRk6duyIsWPHyiUKK3Ofqwldfg8hdt/wufDwcKXvJVUdb6WlpeHo0aM4evQo7t27l+dKEJaWlmjRogUGDRqE5s2bK32M/KSnp2Pv3r3Yu3cvgoKCFG7n4uKCgQMHYujQoUVqnHfy5EnRMV2nTp3QunVrrR7L3t4e06ZNw6JFi+Ti2dnZ2LlzJ3755Zd821DmPlUVt27dwogRI+RiHh4e2LVrl9LH/5wuz/eMjAycPn0aJ06cwI0bNwT3K7kZGhqifv36aNGiBXr06KH2/XhR/T7i+vXr2L59O27evKnwvV+6dGkMHToUo0aNynPiUnp6Onx8fODt7a3wc0AikaB+/fqYMmWKVq8hQOHeCxARERERlQRMniciIiIiIiriDh8+jKVLlyI+Pj7fbV+9eoWff/4Zu3btgpeXV5GrLPThwwesXr0avr6+CpNmcwsODsb27duxb98+jBkzBuPHj9dq1b2srCysX78e27ZtQ0ZGhtbaTUlJwZIlS3DgwAGlklLz8vLlSyxatEjphJfs7Gz4+/vD398fO3bswA8//IBu3bpp1AepVIqFCxfi2LFj+W6bnJyMo0eP4sSJExgzZgymTZtWYqtVK0r8U5Q8n58dO3bgt99+Q3JysibdwqFDh7BixQqFE0FyS05OxqlTp3Dq1Cl4eHhg0aJFqFSpkkZ9yC0uLg5z5swRTSDLTSqVYt++fTh06BCmTZuGr7/+WqNkReDje3Ljxo3YsWOHwoTA3N69e4d9+/bh4MGD+OqrrzBt2jStJ61cvXoV8+fPV5hE+7nQ0FCsXbsWe/bsgaenp9oJk4cOHcLKlStVPk+Tk5Ph5+cHPz8//P7775gxYwbGjx+vVh8Kmr6+PgYMGIANGzbIxfft26eV5PnDhw8LElEqVaqks8R5AKIJdp9WUCgq19xXr15h2bJluHbtmkr7hYSEICQkBHv37oWLiwt27dqFsmXLKr1/YGAgfvrpJ9y5c0ep7WUyGR49eoRHjx5h06ZNmD17Nnr16qVSn5WhrWt8YY6n4uPjsWrVKvj4+Kg8vomIiEBERATOnDmDBQsW4OLFiwVWrfjdu3dYtWoVLl68qPLzL5VKceXKFVy5cgVVq1bFzz//rJOk8aSkJCxevFgwMVNMWloaLl++jH/++QcDBw7EggULNHrfp6enY/Xq1di5c2eeyceftr1w4QIuXLiAvn37YsGCBTqtgl/caXusqCyx5Pk3b94gIiJC6fdd7nuP8uXL5+zr7u4u91hmZibu3r2LL774Qq22P++3MjIzM7Fjxw78/vvvCico5/b+/XscOnQIhw8fRo8ePfDDDz8USDVgRSIjIzF79mzcuHEj322Tk5Nx5MgRHD9+HOPGjcPUqVN1XkW/JH0P8YlMJoOvry/WrFmDmJgYpfZJTEzE6dOncfr0abRu3Rrz5s3TeDWoBw8eYObMmXKrNygSFhaGNWvWYM+ePVi+fLlGicXadPDgQdH48OHDdXK8Pn36YPXq1TkrxHxy4sQJzJs3D2ZmZjo5bnF3+PBhrFmzRql7zU8yMjJw+/Zt3L59G+vWrUOfPn2wbNkylY5bFL+PkEqlmD9/Pk6fPp3vthEREVi9ejWOHj2KTZs2oVy5coJtHj16hJkzZwpWaMlNJpPB398fX3/9Nfr3749FixZpfK9WFL9bJSIiIiIqjoru+oRERERERESE1atXY/bs2Ur9YP25169fY8iQIXj16pWOeqY6f39/9OzZEz4+Pkr/uPO55ORkbNiwAePHj1c6QSI/6enpmDBhAry8vLSaOJ+QkIChQ4eqlViW2/79+9G3b1+VK0V+8u7dO0yfPh2rVq1Suw/R0dEYPHiwUonzn8vKysLmzZsxdepUtV7z4kDR66tqordMJsP8+fOxdOlSjZIq09PTMWvWLMyZM0fpH6pz8/PzQ9++fXH+/Hm1+5FbTEwMvvrqK6US5z+XlpaGFStWYO7cuRqdQ69fv0a/fv2wZcsWpRPnP5eRkYFdu3Zh6NChSldYVsbu3bsxfvx4lZIZgI/JViNHjlTrurBy5UrMmTNHK0l7aWlpGrdRkAYMGCCohurv75/vCgTK8PHxEcQGDRqkcbt5KVWqlCCWlpYGf39/nR5XWbt370afPn1UTpzPLSwsTKVx0JkzZ9C/f3+lE+dze//+PWbNmoW5c+fmWYlWFdq6xgOFO56KiYnBsGHDsH//fo3HN1lZWVp7fpURFBSE48ePa/z8v3z5EiNHjhR9z2vi/fv3GDx4sFKJ85+TyWTw9vbGlClT1P6cTEpKwtixY/HXX3/lmzifm6+vL0aNGiVIZqT/o62xoqoUJaGrMnbIve3nCfPlypVDmTJl5B6/ffu20m2LbVupUiU4ODjku29UVBSGDRuGlStXqnVfKJPJcPToUQwaNAiBgYEq768Nz58/R79+/ZRKnP9cVlYWvLy8dH5/VZK+h/hEKpXiu+++w7x585ROnM/t04Slu3fvqt2Ps2fPYtiwYUolzn8uIiIC48aNw8mTJ9U+trZ8+PBBdJxVsWLFnNUptM3MzEy0IEBycjL+/fdfnRyzOEtISMCECRMwe/Zsle81PyeTyfD06VOlty+q30d8+PABgwcPVipx/nMvX77EkCFDBM/h5cuXMWTIkHwT53M7cOAAZs+erdI+uRXF71aJiIiIiIorVp4nIiIiIiIqon7//Xds3rxZEHd2doa9vT2MjY3x4cMHBAUFiSYfxcTEYMaMGThw4AAMDQ0LossKXb9+HRMnTlS4PLREIoGLiwtsbW1hYmKCDx8+4M2bN6Lb37hxAyNGjMDff/+tcXWxefPm4cqVK4K4s7Mz7OzsYGJigsjISERERCidJJqVlYWJEyfiyZMncnE9PT24uLjAzs4Oenp6iIyMRGRkZJ5J+7///jvWrVun8HFDQ0OUL18eNjY2kEgkiImJwZs3b0QTn7Zs2YKEhATBUuf5kUqlGDt2LF68eKFwG2dnZzg5OUEmkyEqKgrh4eFyj587dw7Lli2DtbW1SscuDhT9ICyW1JqX3377DQcOHBDEHR0d4eDgAHNzc8TExCAiIkJh4l92dja+//77PH9ktrGxQdmyZWFhYYHo6GiEh4eLJpMnJydj6tSp+O2339CxY0eV/pbcMjMz8e233yIoKEgubmhoCGdnZzg6OiIpKQnv3r1TmNB96NAhGBsb4+eff1b5+M+ePcOoUaMQFxencJsyZcrA3t4e5ubmiIuLQ2hoqGgiYEBAAL766ivs2bNH46XODx8+jMWLFwuS6hwdHeHo6AgLCwvExcUhKChI9PqTnJyMGTNm4MSJE7CyslLqmLt378a2bdsUPm5mZoZy5crB0tISRkZGSEpKQmJiIsLDw4tdorwYJycntG/fHmfOnJGLe3t748cff1S73Xv37gmukUZGRlqpaJ8XRVVPPT09sX37dp1XpM3Lr7/+iq1bt+a5jb29Pezt7WFjY4PU1FTExcUhPDxco8lsp06dwowZMxQmkhgZGcHZ2RkODg6QSqV4+/atwmuDr68vkpKSsG7dOo2TXLVxjQcKdzyVnZ2NiRMn5jkecHBwQJkyZWBqagqZTAapVIq4uDi8e/dO42R7XbKwsICTkxMsLS1hamqKlJQUfPjwAaGhoaJjqoyMDPz4448oVaoUOnTooPHxk5OT8d133+H58+dycWNjY5QrVw62trbIyMhAREQE3r17J9rG+fPnsX37dowbN06lY2dmZmLKlCm4deuWwm2cnJxQunRpGBoaIioqCmFhYXLPi7+/P2bOnKnxeKGk0tZYUVWVK1eGg4ODYNKfn58fevbsme/+79+/x+vXr+ViuRPyGzVqhOPHj8u1rQyZTCaaPK/Mai1v377FiBEjEBoaqnAbBwcHODo6wtLSMmccI3atf/v2LYYOHYqdO3eiWrVqSvVdG96+fYsxY8YonJD56Vru4OAAmUyGmJgYhIWFyV1Hz549q7P7q5L0PcQn8fHxGD16NB4/fqxwGxsbG5QpUwbW1tY59yZiSfaf2vLy8lK5CvyNGzcwffp0hWMdExOTnM/xhIQEvHv3Tm4CQ0ZGBmbNmqXSajy6cP36ddHPxx49euj0uD179sT+/fsF8StXrmjl87ikeP/+PUaNGpXnmM3AwAClS5eGnZ0dTE1NkZiYiJiYGI0S7Yvy9xETJkwQPB8WFhZwcXGBjY0NEhMTERQUJDoOj4qKwvfff4/du3dDIpHg7t27mDRpkuB9XLp0aTg6OsLExCTnM1Rs/Hvs2DE0a9YM/fr1U/lvKarfrRIRERERFVdMniciIiIiIiqCbt68iXv37uX828HBAePHj0enTp0ECZuJiYk4cuQINmzYIEgKCAgIwF9//ZVnIo23t7fcv588eSJIsK5ZsyYWLFig1t/y5s0bTJkyRfTHmho1auDrr79Gq1atYGtrK/dYamoqrl27ht9//12QiP7kyRMsWbIES5YsUatPwMfKtJ9XG7azs8M333yDTp06oXTp0nLbpqen48qVK3B0dMy33R07dsi16+Ligm+//Rbt2rUT/I3Jyck4e/as6A9Vp06dEk2cl0gkaNOmDYYNGwZ3d3eYmJjIPR4bG4vTp0/j999/FyRkeHt7o1GjRujVq1e+f8cnK1euREBAgCBuYmKCsWPHok+fPnBxcZF7LDQ0FL6+vti6dWvOD6G7du2Sq1ZZUihKwMj9Wufl4cOHconl5ubmGD16NLp16yZYqjwrKwt+fn4wNzcXtLNlyxaFP1R36dIFw4YNQ6NGjeSSQKVSKc6fPw9PT09BElJmZiZmz54NNzc30WXKleXl5YWHDx/m/NvR0RGTJ09Gp06dBAk/9+/fx65du+QSsT7Zt28fWrRogS+//FLpY8fFxeHbb78VTZiqUKECRo0ahfbt2wuuqxkZGbhz5w68vLxw8+ZNucfCwsIwZ84cbN++Xe2E2sDAQJw6dSrnx3RLS0t8/fXX6N69OypUqCC3bUpKCs6dO4e1a9fi7du3co9FRUVhzZo1WLhwYb7HjI2NxZo1awRxMzMzDBkyBD169ICbm5vovllZWQgODoa/vz8uX76M69eva1y9WRFHR0e5zyUfHx9B0nH//v0xYMCAfNsSW4Z+8ODBguT5o0ePYubMmYLrqbJyf44CwJdffqnzxEhFCVs3btzAzJkzsXDhQqUnVmjTtm3bFCbOly5dGl9//TXatGkjmvyfnp6OgIAAXLlyBadOnVKpcm1ISAjmzZsnmjhfoUIFTJw4ER06dICFhUVOXCaT4e7du9i5c6fgvAA+jhW2b9+OMWPGKN2P3LR1jS/s8dSBAwfw4MEDQbxKlSoYPXo02rZtq/CzTyqV4sWLF/j3339x+fJlPHr0KN/j6ZKDgwPatGmD1q1bw83NDS4uLqLX86SkJNy8eRP79+/H5cuXBY/PmzcPNWvW1DiJcdGiRXJjrSZNmmDMmDFo0qSJ4Lr0+vVr/PHHHzh8+LCgnQ0bNqBbt24q9Wf79u2iq0Po6+tj6NChGDRoEKpUqSL3WFRUFI4fP47ff/8diYmJAICLFy/m/D/J08ZYUV0eHh44ceKEXEzZBHexCRW5K0o3btxYbsz2+PFjJCcn55uE9/LlS9FJBfklz6enp+O7774TTZx3cHDAyJEj8eWXXwrGUtnZ2Xj48CG2b98uuNZ/+PAB06dPx8GDB2FsbJzn8bVl3rx5oonz1tbW+Pbbb9GtWzfBfWdkZCROnDiBTZs2ISEhAYBu7q8K8nuIBQsW5FQ9jo6OxqRJk+Qed3BwgKenp1L9zn2d+pxMJsOsWbNE34tWVlYYMmQIunXrhqpVqwo+CwICArBnzx4cOHBAbnyRmpqKH374AUePHlX6vZyQkIAffvhBNHHe1dUVkyZNQtu2bWFqapoTz87Oxq1bt7Bt2zZcvXoVwMf7lNmzZ6u1mpa23L9/XzRer149nR63du3a0NfXF4z1Pr/XLKoK6nxPTU3FN998ozBxvmXLlhg4cCBatGghNyb+5P3793jw4AHOnTuH8+fP51xvlFFUv4/YsGGD3DnbsmVLjB8/Ho0aNZJblSw9PR0XLlzAr7/+KigIcffuXZw4cQLNmzfH1KlTc97HpqamGDVqFPr06SP47ImJicGff/6Jv/76SzDRaOXKlejUqZPoa6BIYd8LEBERERGVREyeJyIiIiIiKoI+XwK7U6dOWL58ucIkBEtLSwwbNgytWrXCiBEjEBERIff43r17MWbMGIUVaOvXry/3b7HqwhYWFoLtlJGeno5p06YJlgI2NDTEnDlzMGzYMIX7mpiYoEOHDmjfvj1+++03eHl5yT1+4MABtG3bVu0KY58nuLdp0warV69W+MOVkZGR0sf5vN0BAwbgp59+Ek3gBD4mrYpVJg4NDRWtguzg4IA1a9YIqj5+ztbWFkOGDEHPnj0xdepUQULUokWL0LhxY6USq/z8/EQru1WoUAF//PGHIOnvk3LlymHq1Kno3r07xo8fj7CwMAAQXdq9uLtw4YJovE6dOkq3ERgYKLffpk2b4ODgILqtvr6+aMLskydPsGHDBkHc2NgYq1evVlitzcLCAr1798aXX36Jn376CceOHZN7PCkpCT/88AN2794NfX19pf+mz32epPXFF19g9erVChN669evj/r166NLly74/vvvBUkhixYtQrNmzWBpaanUsefMmSOo0iuRSDB+/HhMnTpV4d9kaGiIZs2aoVmzZtizZw9++eUXuSSNf//9Fzt37sTIkSOV6kdunyd4uLu7Y8OGDQoTb0xNTdGzZ0+0bNkSI0eOFCRCHDlyBDNnzsz3h/ezZ88KrsW2trbYtWtXngkYwMfzztXVFa6urujfvz+kUil8fX1V+rFfWUZGRnKfN58ShT5XunRptT6TAKBp06aoWLEigoODc2Lx8fE4ffq0WpXiExIScPr0aUH8q6++Uqt/qnB0dETLli1FE19PnDiBa9euoV+/fujWrRtq1aqlcfV0Zdy/fx+rV68Wfeybb77BxIkT85ykYGRkhLp166Ju3bqYNGkS7t27By8vr3z7npmZiR9++EF0Ukffvn2xYMEC0eNKJBK4u7vD3d0dJ0+exKxZswRJbWvXrkWLFi0UTi7Jjzau8UVhPHXw4EFB7Msvv8Tq1asVjnM+sbCwQMOGDdGwYUNMmjQJgYGB2LlzZ4FWJJZIJGjatClGjRqFNm3aKPV+MDc3R/v27dG+fXv8888/mDFjhlyCeHx8PDZv3qzUBKa8fKrAbWhoiF9++SXPa1HlypWxYsUK1K9fX3DctLQ0+Pj4YOrUqUodNygoSDRRr1SpUti8eTPq1q0rup+jo2POBJCJEyfmJKSKVRL/r0tMTBRNVrewsEDlypV1fnyx5PmQkBBERkbmu4JO7tezbNmyggmzuZPpMzMzce/ePbRs2TLPthUl8Od1jwMAy5cvx9OnTwXxvK7zwMcVwOrXr4/169fj/PnzmDFjhlzy4cuXL7F69WrMmzcvz+Nrw/79+3Hjxg1BvH79+ti4cSPs7e1F93NycsLo0aPRo0cPfPfddzmTmbR9f1WQ30N8Pv78dL/4udxjQnVt375ddAJUmzZtsGLFCtjY2Cjc183NDYsWLULv3r0FE3Kjo6Pxv//9Dxs3blSqH8uWLROdNDFw4ED873//E/0s1dPTy7kn2bt3L37++WfIZDK5cWxhEJtcD3xMbtclU1NTuLq6Cu6HXr16hYyMjCKz0oGYgjrfV6xYITpJ0c7ODr/++itatGiR5/52dnZo164d2rVrh59//hkHDhzAv//+m+9xi/L3Ef7+/gDyH2cZGRmhS5cucHd3x5AhQ/DmzRu5x7dv346rV6/mvI8rV66MLVu2CD4bP7G3t8cPP/yAmjVrYsaMGXJV6OPi4nDy5EkMHDhQqb+hKNwLEBERERGVRIW3di8RERERERHlq0uXLli3bp1SS+hWqFABS5cuFcTDw8NFKwcWhG3btgkqG+nr68PT0zPPH3c+J5FI8P333+Obb74RPCZWmV1VLVu2hKenp9aTQPv27Ytffvkl34QyMT/99JPgRzF7e3t4e3vnm1TyiYWFBby8vARJLVKpFNu3b1eqjWXLlgmWmXZwcMD27dsVJs5/ztXVFX/++Sfs7OyUOl5xExQUJFoh/VOSg6qqV6+Ov/76S2FSZV6WL18uSPjU19fH2rVrlVrm3MzMDCtWrBDd1t/fX/TvVFWDBg2wYcMGpSphd+jQAWvWrBEkN0ZHR2Pz5s1KHe/o0aO4dOmSIL5w4UJMnz5d6R/fhwwZIrryhpeXl+hkI1U0bNgQf/75p1IVK21tbbF27VpBUkhycjJOnTqV7/5iCdY//PBDvonzYiwsLDBixAj07dtX5X0Lm0QiweDBgwVxserxyjhy5Iig+l/lypUF115dmTp1qsKktPj4eGzfvh39+vVDy5YtMWnSJGzZsgU3b94UfMZoQ3Z2NhYsWCCoBiqRSLBo0SJMnz5d5er+DRs2xObNm/NNXD927JhoVfSuXbtiyZIlSh23a9euWLVqleD5zMjIwPLly1XqtxhNrvGFPZ5KSEgQPL/m5uZYtmyZWuMcV1dX/Pzzz/km72pT06ZNsWPHDrRt21atiSStW7fG1q1b5SqUAh+vAapUZVVEIpFg/fr1Sk/iGTx4MLp37y6I+/r6Kn3M1atXCz7HzMzMsHXrVoWJ859zcnLCli1blBoT/lf9+eefSEpKEsQ9PDwE55IuKLpvUKb6fO5txD7XXF1dBeN8ddr+1JaixHHgY1L37t27BfHx48dj2bJlSn++dOjQARs2bBBcB/bs2SOa2KxNmZmZWL9+vSBetWpVbNmyJc+//xMHBwds3boVVatW1UUXcxT37yE+CQ0Nxdq1awXx7t27w8vLK8/E+c81bNgQO3bsEKxOcP78ecHns5iAgADR63P37t2xaNEipT5LBw8ejPnz5yvVX13LnVQMfJxgo+tVl4CPqyLmlpGRIZi88V/06NEj7N27VxB3dnbGnj178k2cz83IyAhDhgxRqiJ+cfg+YsWKFUqNsxwcHASrcQIfJwh8WvmnbNmy2LNnj8LE+c9169YN3bp1E8QPHTqU776fFPa9ABERERFRScXkeSIiIiIioiKqbNmy+OWXX1RK8GnRogUaNGggiBdGxe+0tDTs2rVLEJ8yZQratGmjcntTp05FrVq15GIvXrwQrdynLAsLCyxZskTrFcrKli0rWjleGU+ePBFU9vqUUOXs7KxSW4aGhli9erUg6eHgwYNylVPFPHz4ULSy47x585T6gfCT8uXLY86cOUpvX1wkJCRgypQpguW3gY/vQ1UTBwwMDLBs2TK1JnE8f/5cNAlpyJAhaN++vdLt6OvrY+nSpaKTHcTey6owMjLCypUrVUqc7dixI/r06SOIHzx4UPDDvJitW7cKYgMHDlSrIvigQYMEz2VsbKygMp4qzM3NsWbNGpUST6tUqYJOnToJ4nfv3s1339wV+AGodS0uCfr06SM4F+/duye3coiyxFbnGDRokNp9U1XdunXxww8/5LtdTEwMzp07h1WrVmHkyJFwd3dHp06dMGfOHPj4+Gilgunp06dFK5EOHTpU58+J2DXK0dERixcvVji5QEznzp3Rv39/QfzGjRt49eqV2v3T5BpfFMZTkZGRgsl0jRs31snqE7qijZUX6tevj6FDh8rFkpOTcf78eY3bHjFiBNq1a6fSPpMnTxbEIiIiRCva5hYZGSk6wWzixIkqVQ62tbXFL7/8ovT2/yVXr14VVHf9pEePHgXSh8qVK4tO2MkvwT02NlZu1QxAPHke+LiCjiptA+L3pvlNEBYb17Vs2RLTp0/P93i5ffHFFxg+fLhcLCMjA3v27FG5LVWcP39ekKAvkUiwZMkSpSaXfmJlZaXy9wSqKO7fQ3xux44dgvuGatWqYdmyZSo/f25ubqLn244dO/Ldd9++fYKYvb09fv75Z5X6MXz4cJUToLUtKysLMTExgnhBTZhXNOmYyfPA+vXrBeO1TwnsFStW1Nlxi8P3Eb169RJNYFekWbNmqF69usLHly1bptJ3PqNHjxbEHj16JFhpT0xRuBcgIiIiIiqpmDxPRERERERURI0dO1atpKQuXboIYspUQ9O2w4cP4/3793IxZ2dn0R+NlKGvr48JEyYI4gcPHlSrPeBjIm3p0qXV3l+RsWPHwtzcXK19xarCd+3aFY0aNVKrPScnJ/Tr108ulpycjNOnT+e5n1gV5nr16qFr164q96Fnz56oU6eOyvsVVXfu3EG/fv0Ey8UDHxNgpk2bpnKb7du3F/yAqSyxKpxWVlaYMmWKym1ZWVlh6tSpgvijR4/w8OFDtfoHfPzhvHz58irvN2PGDEFy+fv373Hu3Lk897t27RqeP38uFzM3N8f333+vch8+mTRpkiCmyfVn0KBBKFOmjMr7qXuNF6s0XpwSX7XJ2tpa9FqmavX5e/fuCa4DxsbGSleO1pbRo0dj1qxZKlUxlslkCA4OxqFDh/Djjz+iU6dO6NmzJ/7880/Exsaq1Q+xSpdly5ZVKrlfE/fv3xd9D0yfPl2tc3z69Omin+F///23Wv0DNLvGF4XxlNiEO3XHOcWd2Iob9+/f16hNExMT0dckPxUrVkSNGjUEcWU+Ew4ePCiYAFimTBmMHDlS5X58moxDH6WlpWHLli349ttvBStxAB8TcMU+y3VFLCk9vwR3Pz8/0QkzYnInzz9+/BgpKSkK23716pXgmqaon58EBgbi8uXLcjF9fX3MnTtX7QTy8ePHCyZQazKuU4aPj48g1qlTJ9SrV0/lturXr48vv/xSG90SKO7fQ3wSHx8v+prOmjVLrVVTgI/V33Mn9p48eRLJyckK90lOThadcDtp0iS1nufCnpgulUpFr22qTADRhKLjxMfHF8jxi6rQ0FBcvXpVEB85cqRa1xhVFPXvIyQSCSZOnKjyfooS/93d3dG0aVOV2qpVq5bgu8eMjAzR75RyKwr3AkREREREJRWT54mIiIiIiIogQ0NDtZPvxKpFBgUFadgj1Yktqzxw4EC1f6gGPv54lTtZS5mKy4qIVZfVlJGRkdrVJNPT00WTgpVdhlmRnj17CmL5VQH8559/BLHcSfiq0GTfgvDs2TPcv39f9L9bt27h7NmzWL9+PQYMGIChQ4eKLlUPfHytVKnY+okmz8+1a9cEsS5duqidwNCjRw/BagUARH+MV5a67zV7e3u0bdtWEBc7Pz934sQJQaxbt24KKxUqo2bNmqhSpYpc7NGjR0hLS1OrvQEDBqi1n9hEFGWu8ZaWloKYpgmfxdngwYMFsaNHj6r0eopVne/UqRNsbGw06ZpaxowZg7///luj5Jjnz59j+fLl6NChAzZt2oTU1FSl942NjRX9XBk8eLBKK06oQ+waaGlpqdZkLwAoVaqUaCKw2HGUpck1viiMp8Q+Tx49eoTs7Gy1+1Bcubm5CRJuNb2Wtm/fXu3PJ7ExhzIrSVy5ckUQ69mzp9rnlS7G1EWJVCpVOE709/fH1atXcejQIfz0009o27YtVq1aJbpKjrGxMRYsWKCziuFixJLSg4ODERUVpXCf3Mn1Dg4OCqsW524/IyMD9+7dU7rtT5o0aaJwn1OnTgmS+Zs2bSoYl6nCwcEBzZs3l4tFRkYiNDRU7TbzkpWVJfq8iK2ypCxN9lWkJHwP8cnFixcFSe0VK1ZEq1at1G7T2NhYMEbIyMjIM6n33r17gkmsxsbGan9nUK1aNZ0nQ+dF0fiwoCblit3TAFD7nqykOHv2rOA6aWBggFGjRun82EX9+4hGjRqpVXm/WrVqonGxiZTKEKtkr8yYrSjcCxARERERlVRMniciIiIiIiqCatasqXZFzwoVKghiYhWHdSk9PV30B2RNq+Pp6+sLloN/+/atWkt029vbw9XVVaP+iKlRo4baPxI+fPhQ8KOvg4OD4G9WVc2aNQU/PuaV1PLu3TtER0fLxSQSiUZVRTt37qz2vgVh0qRJGDRokOh/I0aMwOTJk7Fx48Y8EyO+/PJLzJs3T+Vj6+npKazmmZ/o6GiEh4cL4qosSZ6bmZkZOnToIIirmxxYpUoVVK1aVe3+iJ07+VWdu337tiCmjeqcuVeAyMjIwKNHj1Rux8HBAZUrV1arD05OTjA2Nhb0I7+EEbHXYMmSJYiLi1OrH8Vd3bp1BZXA4+PjcerUKaX2T0xMFF3BY9CgQVrpnzoaNGiA/fv3Y/PmzejQoYMgwVdZSUlJ+O233zBgwAClkwj//fdfQSK1RCIpkIRaf39/QaxDhw6C94kqxCadhYaGIiYmRuW2NLnGF5XxVLly5QTP55s3b7B+/XqN+lFc5a46rGmyrbrnBwDRVV3EVgr4XEZGBp4+fSqIazJWa9GiRYFVHS4MT58+VThO/OqrrzB27FjMmTMH3t7eolXVgY/XglWrVqFhw4YF2ndFSel5VZ/PPY7Kqyp8tWrVYG1trXTbYo9VqVJF8L7Kqz+AdsZ1uavmA3nfI2ni5cuXgkRuIyMjtGjRQu02W7ZsqVHypJji/j3E54rKefPgwQNBrEWLFholmxfk6hW5KZo4p+1zURFFxxGbsPRf8u+//wpirVq1gpOTk06PWxy+jxB7zyrD2dlZNK7uqpBi7eU3Zisq9wJERERERCUVk+eJiIiIiIiKIE2q6In9CJvfDzLa9vjxY0FFMnNzc7WTRT8n9oPT8+fPVW4nd9KktmjSrljV3tq1a2tcndLAwEDwo+mbN2+QkpIiur3Yj3MuLi4aVVMuVaqUwh8fiztDQ0NMnjwZa9euhZ6e6l+1VK5cWbSymjLEkjH09PREq5Oron79+kodSxma9kVRpXVF1zVFVUPVWREgNxcXF0FMneuPphN31LnOt27dWhALCAhA9+7dsWPHDsTHx2vUp+JIrPq8WDV5MUeOHBFcQ11dXdVOztCm1q1bY+PGjbh+/TpWrVqFQYMGwdXVVeXPkhcvXqB///4IDAzMd9snT54IYq6urhqt9qAMmUwm+pmlaUXWOnXqiD5f6iTtaHKNLyrjKWNjYzRr1kwQ37RpE0aMGIErV64Uyyr0kZGR8PHxwS+//ILhw4ejY8eOaN68OerWrYvq1asr/C93YlFycjIyMzPV7ocmE8zU+Tx4/vy5YMKVkZGRwgqrytDX14ebm5va+5d0Li4u2Llzp1YSd1VVqVIlODo6CuKKEtxjY2Px8uVLuVheEzz09PQEEwLySp4Xu9/JKzk/MzNTdAyqjXGdtu4rlfH48WNBrGrVqmpPdAM+3oNo8r2BmOL+PcTnxKooF8Z5IzbRVtN+6Oq7DGUoSl4vqIkSio6j65WOijqxa0xB3JcUh+8j1B03i00kMjAwUKuKvaL28rtGFpV7ASIiIiKiksqgsDtAREREREREQrmr96lC7EfDgl7COnfCBfCx0rK6laI+J/a3qFM5uXTp0hr3Rdvtij1vpqamWnnexBIz4uPjYWpqKoiLVQ7TRkKUm5ubaNvFlYmJCbp06YJRo0Zp9Pxocs6EhYUJYhUqVFA7UfOTGjVqCGLx8fGQSqUqV0kUW55cFS4uLjA3N0dSUlJOTCaTISwsTLSfYu8jMzMzhISEICQkRKO+JCQkCGLqJJ1rco0H1LvOd+zYEZUrV8br16/l4tHR0Vi6dClWrlyJxo0bo3nz5nB3d0ft2rULrIJkYenevTtWrlwp97revXsXr169yjd5zNvbWxArzKrzYqytrdGjRw/06NEDwMfkjKdPn+LZs2d49OgRbt++jcjIyDzbiIuLw8SJE3HgwAFYWloq3C44OFgQq1u3rkb9V0ZiYqLo+1Ls2qAKCwsLVKhQQfB3vX37VuW2tD0uKKzx1Pjx43H58mVB/NatW7h16xZsbW3xxRdfoEmTJnB3dxetiF5UXLx4EX/99Rdu376ttaT/hIQEtSeLaHvcn56enuc+YmOxKlWqwMBAs5+L3Nzc8kya/i9ycXHBgAEDMHz4cLWreWuDh4cHjh8/LhdT9FrduXMHMplMLpbf6gju7u64dOlSzr8fPXqElJQUwX1GYGCgYHWrT/1TJDQ0VHTCb0JCgsbXQrHVRHQ1mVDsWNpIfHd1dRVdSUJdxf17iE/S09NFxyYpKSkanzdiY4G8zhtd3FsX5mQlse8PgIKbKCE27gMU9+u/ID4+XnQMp+lkUmUUh+8j1C0CIbaKlSar7Ii1l9+YrSjdCxARERERlURMniciIiIiIiqCNP2hqbCJ/eASHByss8RCdZIcNFkmXVftij1vJ0+exMmTJzXoUd7HE0vqE3s+tVE92M7OTuM2CoORkREsLS1haWkJR0dH1K5dG3Xq1EHLli01+vH0E03OGbHkAbHqoqpS1EZ8fLzK/XVwcNCoLxKJBPb29nLJ84DixAmx91FycnKRuv7o4hqfO9EtN319faxduxaDBw9GcnKy4PHMzEzcuHEDN27cAPBxwk3t2rXRpEkTNGnSBI0bN9aoOmpRZGpqil69emHXrl1ycW9vb8yfP1/hfv7+/njx4oVczNjYGL1799ZFN7XG0tIy5/X85NWrVzh+/Di8vb0RGxsrul9wcDA2b96MGTNmKGxbLAlfV5PUPqfo/aet62DuxDtF1528aHtcUFjjqUaNGuG7777Dxo0bRR+PjY3F4cOHcfjwYQAfxw2NGjVCkyZN0Lx5c41X3NCG6Oho/Pjjj6KTADSlaDUfZWg74S+/zwOx8/i/PM7TBjMzM1haWsLKygqVK1dG7dq10aBBA7i7u2u8gpQ2iCXPBwUFITo6WjBOy51Ub2trm2+Sd+7k94yMDPj7+6N58+Zy8du3bwv2lUgkcp9LuSlK5Pv666/z7JO6dJU8L/a+08Z9hDba+Fxx/x7iE0XnzezZs3VyvLzOG7HHNL1eWllZwdDQEBkZGRq1ow4zMzPBxGZAvTGSOhRVntfG2K+4UjQZtiDG4sXh+whtrkpQ0GO2onQvQERERERUEjF5noiIiIiIiLSuoKsVqVPlTFeVyTRp98OHD1rsSf4U/fAs9nxqY7KBriYsaMOFCxfg4uJSKMfW5JwR+7FaG8+zogrTCQkJost750VX544qyfO6VFBVFrXBzc0N+/btw5QpU0SrcX7uU+Kbv78/vLy8YGNjg86dO2PkyJFaWSa+qBg8eLAgef7IkSOYOXOmaHVAANi/f78g1qVLF41XFCgMVapUwbRp0zB+/Hhs2rQJW7ZsEU3i+PvvvzF27FiFf6PYhIyCeD4UXQd0dd1RJ6FEk2t8UbueTZkyBdbW1li1alW+lTJjY2Nx7tw5nDt3DsDH60+fPn0waNCgQqkOGxkZiZEjRyIoKEgn7eeX/FSUFPTYoSTw8PAQfFYUJ4oqu9++fRtdu3aVi926dUvu3/lVnQeAmjVrwszMTO6zwM/PT5A8L1btvkqVKnlO3ihq10F1id13aWM1gpL8vtNEUTpvdHlvXdDfH3xSunRpBAYGysVCQ0ORlZUFfX19nR5b0ee4k5OTTo9blImNwwHtT64RUxy+jyjOitK1jIiIiIioJNIr7A4QERERERFRyVPQP7hkZ2cX6PF0RVEyu65kZWWJxsUS4rRRcdrIyEjjNkheamqqIKaNymqK2lD0w3xeFCUga9qGoiq/vP7krXr16jh27Bj+97//oWLFikrvFxcXh3379qF79+6YO3dugV+vdMXV1VWQVBgfH4/Tp0+Lbp+YmIhTp04J4gMHDtRJ/wqKmZkZZsyYgeXLl4s+npycjGvXrincX+x9oI33fn7EroGA7q6DmlQXV0dRvJ6NHDkSp0+fxsCBA1WqUBwQEIBly5ahffv2OHTokCbdVFl2djYmTpyoMOHOxsYGbdu2xYQJE7Bw4UJs3LgR27Ztw99//w1vb2/Bf5quqFLYOM7776lUqZJoFd/cifJxcXF4+fKlXEyZ5HkDAwM0aNBALiaWKC9WeV5RYv8nRfE6qA49PeHPsYruxVRRGJXHi4OidN6UxGuu2GoUKSkpgoR6XXj8+LEgZm9vr5UVVIorRdcSbVZcV6Q4fB9RnBWlaxkRERERUUnEyvNERERERESkdWLJAZS/ovK8iVX5yr0suzq00QbJE6vqpsvXSp3qlrrqj6K+6LraYUlgZGSEYcOGYdiwYfD398eVK1dw69YtPHr0KN9q0llZWfD19cXdu3exa9euElHlcciQIYIkP29vb/Tq1Uuw7ZEjRwQJ1FWrVkWjRo102seC0rt3b1y9ehXHjx8XPHbz5k1069ZNdD+x92NBTLBQVNlSKpVqnESlynVHV4rKuCA3Z2dnLF68GHPnzsXly5dx48YN+Pn55buiBQC8f/8ec+bMwYMHD7Bw4UKd9xUAfHx8RJPtqlevjqlTp6Jt27YqPddF9XVRlq7GeSVlUlVJ5eHhIbi25/7su337tmAVBWWS5wHA3d0d169fz/n3w4cPkZqampMAGRQUhKioKMF+TZo0ybPdkjKuE6sArY33DN934orSeWNhYSGoHl3cr7m1atXCmTNnBPFHjx6hWrVqOjtuaGioaCXuGjVq6OyYxYGiCvNSqVTnk1mLw/cRxVlxH3MSERERERV1TJ4nIiIiIiIirRNbgr579+5YvXp1IfSm+BB73hYvXlzgFY11lQTJJaC1T1eJOIpeK2tra5Xb0lVikKIfzsXeRw0aNMC+ffs07kdJ1KBBg5xqsenp6Xj48CHu3LmD27dv486dOwore4eEhODbb7+Fj49PkUpQUkeHDh3g4OCA6OjonNjdu3cRGBgIV1dXuW33798v2H/QoEE672NBGj58uGjyfEhIiMJ9bGxsBLGCuOYruiYlJSVpnDwv1n91roGaKOrjKTMzM3Tt2hVdu3YFAMTExODu3bu4ffs2/Pz88Pz5c4X77t27FxUrVsSoUaN03s+///5bEPPw8MDmzZthamqqcnsJCQna6Fah4Tjvv0ksef7169eIiYmBvb09AGEyvbW1NapXr650+5/LyMiAv78/mjVrBkC86rxEIsk3OV/sOujo6IirV68q1a+iQmzMHhsbq3G7Hz580LiNkkjsvNHT08ODBw8KvGK7lZWVIOFb0+tldnZ2oVbgVrRixLVr19CvXz+dHVfRKkhNmzbV2TEVyczMLPBjKqJofJqQkAA7OzudHrs4fB9RnBX1ewEiIiIiouKO01WJiIiIiIhI68qUKSOIhYaGFkJPihex5+3NmzcF3o9SpUoJYspUk82PNtogeWI/HmvjvabovFPnx+qwsDCN+pKRkSFaqVQsWRcASpcuLYjx+qMcIyMjuLu7Y8KECdi2bRtu3boFT09PtGzZUnT7J0+e4PDhwwXbSR0wNDRE//79BXFvb2+5f9+/f1+QDGxiYiJaob44q1OnDgwNDQXxvBL0xN6PBXHNV1RpUxvvebE2FB1PV4rbeMre3h6dOnXCjz/+iKNHj+LSpUuYOXMmnJ2dRbf39PREfHy8TvsUGhqKFy9eyMUMDAywcuVKtRLn09LSBKtPFDcc5/03KUp2/TxhPnfyfKNGjSCRSJRqv27duoIKx3m1DXxcuSW/iU5i47ro6GiFk/uKqrJlywpieU0wUlZAQIDGbZREYp+f2dnZhfIZKnbNzWtCojKCg4MFq0QUpHr16on+XefPn9fphI4DBw6Ixlu3bp3vvmKTfbOystTui1gF/MJiY2Mjeq0uiM/l4vB9RHFW3O4FiIiIiIiKGybPExERERERkdZVqVJFEAsMDERGRkYh9Kb4yF3hGCichAg3NzdB7Pnz5xr9QJ+dnS1IXiPNiZ0zkZGRGleyfPbsmSDm7OwMExMTldvS9BwWu3YYGRmhfPnyotuLXX9iYmIQExOjUT/+i0xMTNCxY0ds27YNXl5eoommR44cKYSead+gQYMESTVHjhxBWlpazr9zJ9MDQJcuXQo8oVrX9PX1RZPh80owqlGjhiB2//59LfZKnJmZmWhC4tOnTzVqNzo6Wm4lgk/Eri+6VNzHU2XLlsW4ceNw6tQp0RUaEhMTcenSJZ324cmTJ4KYh4eHaDKSMh4/fqxplwqd2DgvKipKJ2MHKjoqVaoER0dHQfxTUnt8fLxgrK4o4V6MkZER6tatK9o2IF55Xpn2K1asCAMD+UW0ZTKZVhLPC1KdOnUEsZCQEI0mEMXFxTGJUgELCws4OTkJ4oVx3ohdczW9Xhb2pAk9PT10795dEE9PT9fZvUFAQIDoZ3DNmjVRtWrVfPcXq+CdlJSkdn8iIyPV3lfbjIyMRL8XKIixeHH4PqI4K+73AkRERERERR2T54mIiIiIiEjr6tSpI0hClEqlohUH6f80aNBAELt165ZGP+qqo2bNmoKqw8nJyRr9+Hr//v1CXVq+pKpbt65oFT1N32u3bt0SxMTOT2X4+/trVNXvzp07gpibmxuMjIxEt69QoYJoFdOLFy+q3QcC2rZtiylTpgji/v7+Oq18qWzFW02VKVNGULUyLi4Op0+fBvAxyffUqVOC/cQSgksCsc8dOzs7hduLJUBGRkbi1atXWu2XmPr16wtiYkmaqhC7BhoYGIgmP+pSSRlPGRsbY+HChaIJhHfv3tXpsd+/fy+IiSV6KUvX/S0Ijo6OopMH/v33X7XbjIiIYOX5YkDsWv3penL79m1kZ2fLPda4cWOV2nd3d5f798OHD5Gamoo3b94gIiJCsH2TJk3ybdPExET02lHcxnXlypUTfI7KZLKccYY6Tp8+XajVx7VJF+M9sfHBhQsXtH6c/OSeVAIAN27c0KhNTffXhoEDB4rGt27dioSEBK0fb82aNaJxZcfilpaWgpjYREll3bt3T+19dXG+i12vr1+/rvXj5FYcvo8ozkrKvQARERERUVHF5HkiIiIiIiLSOktLSzRq1EgQP3z4cMF3phhp2rSpoLJzeno6Tp48WaD9MDY2Rs2aNQXxY8eOqd1mSalOXdSYmZmhevXqgvjRo0fVbjMqKko0IUMsAUUZMTExuHnzptr9ETvv6tWrl+c+bdq0EcR4/dFct27dBLH09HSNqqbmR2ySRGZmpk6ONXjwYEFs//79AD6+p1JSUuQeq1atWolM4oiIiBCd7GRvb69wn1q1aolW9Ny7d69W+yZG7DW4du2aRhUvxa4X1atXF119QZdK0nhKT08PnTt3FsQ1SVxTRmJioiBmZmamVlsymQyHDh3StEtFgtj7RpNx3tGjRwWJ11T0iCWrBwYG4v3794JJRxYWFqKriuQld/Jmeno67t+/L5oEKZFIlE7Ob9u2rSB2/PjxYld5N/ckPeDjOEOdBHiZTJYzRikJxMZ7mr6+YufN5cuXdTpuFSN2vQ0KCsKjR4/Uai89PV2jSRfaUq1aNXTs2FEQj46Oxq+//qrVY504cQL//POPIF6mTBn07dtXqTbEVt5QdyWCpKQkje5vdXG+i13fHz16pPMVc4rD9xHFWUm6FyAiIiIiKoqYPE9EREREREQ60bt3b0Hs+PHjhb7EeFFmYmKCTp06CeIbN25EWlpagfalV69egtjhw4cRFRWlcluRkZEa/XhKeRM7Z65cuYKgoCC12tuxY4egUryBgYFocoSydu3apdZ+jx49El3xoGvXrnnuJ3b+3r17F5cvX1arH/RRqVKlROO6TJgUS8jW1SoWrVq1Qrly5eRid+7cQWBgoGiCWkmtOq9owlZeEwX09fVF33e+vr54+/at1vompkOHDoKKjJmZmfj777/Vau/FixeiFbjFEr8LQkkaT4ldQ3SdcC1WaVadsQzwsdL169evNe1SkSD2fr169apa51VycjJ2796tjW6RjolVngc+VgjOXcW2YcOGotWE89KgQQPB6lV+fn6iq4FUrVpV4bgit549e0JPT/7nzLCwsGKXPC42bnj8+DF8fX1VbuvgwYN48uSJNrpVJIiN93JPWlRVx44dBZOlpFIpvLy8NGpXVa6urqhdu7YgvmXLFrXa27t3r04qu6vj+++/F7znAcDHx0dr913v3r3DkiVLFB5f0WpkuYkVB7h27ZpaffLx8RGdnKcsXZzvbdq0Eb2menp6atSuMorD9xHFWUm6FyAiIiIiKmqYPE9EREREREQ60b17dzg4OMjFsrOzMWvWLEil0kLqVdE3atQowTLe7969w8KFCwu0Hz179hRU2E1KSsKKFStUbmvp0qU6S3YlYODAgYKkgYyMDPzyyy8qtxUYGIgdO3YI4h06dEDp0qXV7uOlS5dw9epVlfaRyWSiiRJVq1ZFw4YN89y3adOmqFWrliC+YMECREZGqtQP+j9v3rwRxAwNDZVOflOHlZWVIBYeHq6TY0kkEtHEtgULFgiSE0xNTUWTTwvS3r17sX79eq1WUI2OjsYff/wh+li7du3y3Hf06NGCRMvk5GTMnj1bpwnSZcuWRfv27QXxrVu3IjQ0VKW2ZDIZFi1aJEjYMTY2xoABAzTqp7pK0nhK7Bri5OSk02OKVZq9fv26yitYxMfH4+eff9ZWtwrdF198gTJlysjFsrKyRM///Hh6eiIiIkKb3SMdqVixouh77vz584LPOWWrwn/OzMxMkKCqKHlerEqyIuXLlxe9zq9Zs0btytGFoX79+qLj02XLlqmUCP/48WMsX75cm10rdGZmZoIk7MTERI2SxC0sLDBw4EBBfNeuXaKT5HRJbHx55swZlZO3o6KisGHDBm11S2Ourq747rvvBHGZTIbvv/9eMClHVdHR0Rg3bhzev38veOyLL75QaSxep04dQSwsLAzXr19XqU9v3rzR+DXQxfluamqKYcOGCeKXLl3CgQMH1G5XGcXh+4jirCTdCxARERERFTVMniciIiIiIiKdMDY2xowZMwTx58+fY+LEifjw4YNG7WdnZ+PixYtqL3deVNWoUQN9+vQRxH19fbF8+XKVE5pyS05Oxp49e/KtlGZpaSmaKHj8+HH8/vvvSh9v3bp1RWJZ+ZLM1tYWPXv2FMSvXbuGtWvXKt3O+/fvMXHiRNEl40eOHKlRHwFg9uzZCAkJUXr7ZcuWwd/fXxAfPny40sfLLSIiAmPHjtVK8rWfn1+BJ96oSyqVYuHChSo9/2LEEhlq164tmPCjTVWqVBHE7t+/r7Nk7H79+gmSP8SS/rp06SJa0bogJSYmYuPGjWjXrh3Wrl2Ld+/eadReZGQkRo8ejbi4OMFjzZs3h4uLS577lytXDj169BDE/fz8MGPGDKSnp6vVL6lUiqSkpDy3EbtGpaWlYeLEiSpNLli6dKno6927d2+dThLJS1EYT505cwa7d+/WqCpqXFyc6Co0YtV4talRo0aCa1RUVJRKldKlUim+++67EjX5Sk9PD6NGjRLE7969iwULFijdzoEDB7Bt2zYt9ox0TSwp/tSpU4LPVUVV6vPj7u4u9+979+6JrkCiSvI8AMyYMUOQbCqVSjF+/HitVN99+vQpzp49q3E7+fnf//4nqKKfmJiI0aNH49KlS/nuf+HCBYwZMybnXk6XY7CC5urqKojdu3dPoza//fZb2NjYyMUyMjIwZcoU3Lp1S6O2ASA4OBiHDx/Od7vu3buLTlyZPn260hNAEhISMHbsWI0qnuvC+PHj0bJlS0E8OTkZY8aMgbe3t1rtPnz4EIMGDcLLly8Fj5UtW1blSf3Ozs6iY44lS5bkO878JCIiAhMnTtRKwrIuzvdhw4bB2tpaEP/5559x6tQptdvNb/xTXL6PKK6Kwr0AEREREVFJxeR5IiIiIiIi0pk+ffqIVgm8desWevfujXPnzqmcABkSEoKtW7eic+fO+Pbbb0WrmBZ3c+fOhbOzsyD+559/Yvjw4Xj8+LFK7clkMty/fx/Lly9HmzZt8PPPPyM1NTXf/aZNm4ayZcsK4uvWrcP8+fPz/OE+ISEBc+bMkUu0z13JnrRn5syZgmpkAODl5YXFixfnW/n/yZMnGD58OIKDgwWPDR48ON9K73n5lCD0/v17jBw5Mt8KhMnJyViwYIFoonb9+vWVrv7cpEkT0UT7Fy9eoE+fPjhw4IDKybyRkZHYvXs3evfurdZ7sbBkZ2dj79696Ny5M7777jucPXtWqWvAJxkZGfjtt9+wf/9+wWNiydLaVKVKFRgbG8vF3r9/j7179+rkeLa2tujcuXO+23311Vc6Ob46pFIpvLy80LZtW4wYMQK+vr4qJVFIpVLs2LEDPXr0wIsXLwSPGxoa4scff1SqrR9//BHly5cXxE+ePIkRI0aolJTx7t07rFu3Du3atcv3s97d3V20uuyLFy8wYsSIfBPTpFIpfvzxR+zcuVPwmJOTk2jCSkEq7PHUu3fvsGjRIrRp0wa//vqrShWSAeDt27cYN24coqOj5eKGhobo1KmTSm2pytbWFg0aNBDEV65cqVTC49OnTzF8+HC5SRW5V1goroYPHy6aSOjj44MJEyYgKipK4b5paWlYuXIl/ve//+XEOM4rHsSS1nNPzjUzMxOtkK6M3Mn5YhN/JRKJypXtK1WqhJkzZwriERERGDhwILZt26bySlcfPnyAr68vhg0bhj59+qhcBVwdDRo0EB2fxsXFYcKECRg9ejQOHTqEN2/eIDk5GUlJSQgJCYGvry9Gjx6NiRMn5kxyMzMzQ+/evXXe54Iids5t3rxZ7cl3AGBjYyNa/ToxMRGjRo3C6tWrRScN5kUqleLUqVP45ptv0LlzZxw7dizffczMzEQnJsXHx2P48OH5fh75+/tj0KBBOeMZPT09wfi4sOjr62Pt2rVwc3MTPJaeno6ffvoJw4cPV7oKfWhoKBYuXIjBgweLTni2sbHBpk2bYGtrq3Jfxe4jAwMDMXbs2HwnoZ4+fRoDBw7MSebX9DNPV+f78uXLBZNq0tPTMX36dKxcuVLp6vYymQy3b9/GxIkTMWHChHy3L8rfR5QEhX0vQERERERUUhkUdgeIiIiIiIioZFu5ciWGDx+Op0+fysUjIiIwadIkVKhQAZ07d0bDhg3h6uoKGxsbmJmZITk5GQkJCYiJicHz588REBAAPz8/0cpjJY2VlRU2bdqEYcOGCX7cvHv3Lvr16wd3d3e0bt0aDRs2hIuLC6ysrGBoaAipVAqpVIqwsDA8f/4cz549w/Xr1wUJa8owNzfHokWLMG7cOMhkMrnHDhw4gDNnzqBjx45o0qQJHB0dIZPJEBUVhZs3b+L8+fNyFdnKli2LTp064c8//1TvSaE8lSpVCsuWLRN9rf7++29cvHgRffr0Qbt27VC2bFlYWFjkvLdOnDiBU6dOITMzU9BupUqVRCu4q2Lo0KHYtWsXgI8JmCNGjEDHjh3Ro0cP1KxZE46OjkhKSsK7d+9w8eJFHDx4ULRCqaGhIRYvXiyo1pmX2bNnIzg4GFevXpWLx8fHY/78+fjtt9/QrVs3NGrUCNWrV4e1tTUsLS2RkpKCxMRExMbG4sWLFwgICMDdu3fx+PFjwfNbnGRnZ+P8+fM4f/48zMzM0Lx5c9SpUwc1a9ZEhQoVYGVlBUtLS2RmZiI+Ph7BwcG4desWDh8+LJq84urqiv79++u0z4aGhmjXrp2gWuKiRYtw5coVfPHFFyhfvjzMzc0F54aRkRFq1qyp8jEHDx4sWiH7k+rVq6NevXoqt6trMpkMt27dyqmkWqlSJTRo0ADVq1eHra0tbGxsYG5ujpSUFCQkJOD169d4/Pgx/v33X6SlpSlsd/bs2aLVMcVYWlpi3bp1+OqrrwRt+vv7Y8CAAWjZsiXatm2LRo0awd7eHtbW1khLS0NcXBwCAwPx6NEjXLt2Dffv31fp/TZ37lz4+fkJkm4CAgLQr18/dO3aFV27dkW1atVgb28PqVSK8PDwnOuOWFVNiUSCFStWiFbxLGhFYTwVFxeHrVu3YuvWrShXrhyaNWuGWrVqoUaNGnBwcICVlRVMTU2RkpKCqKgovHjxApcvX8aJEydEk8EmTJggqAisCxMnTsTYsWPlYpmZmZg9ezYOHz6MAQMGoH79+nBwcEBmZiZiYmLw8OFDnD17VpCM1KtXL9y5c0crK5gUNn19fSxZsgQDBw4UvF8vXbqEjh07ok2bNmjZsiVKly4NAwMDREVFwd/fH2fOnEFsbGzO9hYWFhg9ejTWr19f0H8GqUiZivINGjQQVHlXlru7O/T09PJM4qtWrZpa7/1Ro0bh5cuXOHDggFz802SOP/74A126dIG7uztq1qyJUqVKwcrKCmlpaZBKpfjw4QNevXqFgIAA3L9/H/fu3dN4VS91zJw5E0+fPhVd6eT69eu4fv26Uu0sWrRINNG0uFaj79SpEw4ePCgXu3v3Lrp3746ePXvCzc0N1tbWoudmlSpVYGFhIdpux44dMXXqVKxbt04unp2djc2bN2PXrl3o1KkTPDw8UKdOHdjZ2cHKygqZmZlITExEfHw8Xr9+jWfPnuHRo0fw8/NTK8G5ffv26N69O44fPy4Xj4+Px+zZs7F582Z06dIFVatWhZ2dHeLj4xEWFoZz587h7t27cmOi4cOH4/z580Xms8jKygo7duzAmDFjRCcX+/n5Yfjw4ahQoQJatmyJGjVqoGzZsjA1NUV6ejpiY2Px8uVL3Lp1C/fu3VM4/rO1tcW2bdtEE/WV0bdvX+zcuROBgYFy8Xv37qFLly7o0qULmjZtCkdHRwBAbGwsHj9+jMuXL+P169c525uamuKHH37AokWL1OoHoLvzvV27dhgzZgy2bt0qF8/Ozsa2bdtw4MABdO7cGV988QWqVq0KW1tbmJiYIDExETExMXj69CkePnyI8+fP54yNlXm+i/L3ESVFUbgXICIiIiIqaZg8T0RERERERDplYWGB7du349tvv4W/v7/g8ZCQEPzxxx+F0LOirXr16tixYwfGjx8vmvh+584d3LlzR+f9aNWqFebOnYulS5cKHktMTISvry98fX3zbMPU1BTr16/H5cuXddRLAj6+VnPmzMGyZcsEj719+xYbN27Exo0blW7P0dERf/zxh8ZV9Tp27IjMzMycSuEymQxnz57F2bNnlW5DIpFgyZIlqFatmkrHNjQ0hKenJ6ZPn44LFy4IHo+OjsZff/2Fv/76S6V2S4Lk5OScRHp1WFlZYdWqVQVS9XL48OE4ffq0IBHj8uXLeV5XnJ2dcfHiRZWP17BhQ1SvXl1htfJBgwap3GZhCAoKQlBQkNr7SyQS/Pjjjxg2bJhK+9WsWRMbN27E5MmTkZKSIveYTCbD1atXBRNatMHMzAybNm3CiBEjBJ+bGRkZOHLkCI4cOaJSm//73//QrFkzbXZTbUVtPBUaGorQ0FC192/WrJlSlUy1oVWrVujZs6fopJgbN27gxo0bSrVTq1Yt/PTTT+jZs6e2u1ho3Nzc8Ouvv2L69OmCpLXU1FScPn0ap0+fzrMNPT09rFq1SqUVL6jwVKxYEU5OTqIThj5xd3dXu31LS0tUr14dz549U7iNWPV7ZS1evBiGhoaiK9DEx8dj37592Ldvn9rtFwQjIyP88ccfmDJlilrV7vX09DB//nz06NEDv/32m+Bxc3NzLfSy4LVq1QqVK1eWS1IGPn6+bdiwIc99d+7cmed5NXHiROjp6WHt2rWCx1JSUnD48GGlViPR1JIlSxAeHi76OR4YGAhPT89823B3d8fMmTPVHsPrio2NDXbv3o2FCxfi0KFDotuEhIQgJCRErfZr164NT09PlClTRu0+GhkZYenSpRg2bBgyMjLkHktJSVHquw1DQ0OsW7cOJiYmavcD0O35PmPGDCQkJIiuGhYfHw9vb294e3ur1/E8FNXvI0qKonYvQERERERUEihfqouIiIiIiIhITaVKlcLOnTsxatQolapGK8PAoOTOC69ZsyYOHjyIli1barVdiUQCfX19pbcfOXIkfvnlF7UqUFpbW2PLli2oU6eOyvuS6kaNGoWVK1dq/GN+zZo1sW/fPlSoUEEr/frxxx/VTjY0NDTEsmXL0KtXL7X2NzExgaenJ2bMmKF2FVVFSvL1Jy9ly5bF7t271arqro5GjRph3LhxBXKsTwYPHiwaNzMzU/tc1LaqVauiXLlyOmm7WrVq+Pvvv1VOnP+kVatW2LdvHypWrKjdjuWjcuXK2Lt3r9oVST8xNTXFmjVrMHToUC31TDtKyniqW7du2Lx5c4Eec8mSJWjevLna+9evXx9bt25VWOm1OOvUqRM8PT3V+tuMjY2xdu1atG3bVgc9I11p3Lhxno8rU50+L/kl32uSPK+np4eFCxdi6dKlWk8S1/Y4MS/m5ubYsmULZs6cCTMzM6X3c3FxwbZt23I+nxMTEwXbFNfrlJ6eHlavXq3xfYwiEyZMwB9//AF7e3uttqvKeWNiYoLt27erfc1s2rQpvLy8YGRkpNb+umZiYoLly5fDy8sLLi4uWmnT3NwcP/zwA7y9vTVKnP+kfv362Lhxo1rnmaWlJby8vNC6dWuN+6HL811PTw+LFy/GvHnzCvxcKarfR5QUJeVegIiIiIioqGDyPBERERERERUIIyMjzJ07F4cOHUK7du00+qHHzs4Ow4cPx4EDB9CpUyct9rLocXJywrZt2+Dp6YlatWpp1FblypUxbdo0XLx4Eba2tirtO2DAABw4cEDpZBqJRIKOHTvi6NGj+SbokHb16tULx44dQ/v27SGRSFTa19raGtOnT8f+/fvh7OystT4ZGBhg5cqVmDdvnkoJQm5ubti7dy/69Omj0fH19PQwfvx4nDp1Cj179tQoOcrS0hJ9+/bFzp078fXXX2vUr4JiZmaGGTNmoFGjRipNnBFrZ8KECTh58qTKqwBoasaMGVi0aBFsbGwK5Hg9e/YUPVe7du1aZJLS2rZti/Pnz+PYsWOYNm0aPDw8NEpU0dPTQ9OmTbFs2TIcOnRIo8rDwMf377FjxzB9+nSUKlVKrTZsbW0xevRolSYJlCtXDj4+Ppg6dSqsrKxUOt6nz65jx46hW7duqna3QBTGeKpVq1YYOnSoxp8LlStXhpeXF9asWVPgyVxGRkbYunUrxo8fr9JngKGhISZMmIBdu3apPHYqTtq2bYvDhw+jQ4cOSu/j4eEBX19fdO7cWYc9I13IK3nd2NgYdevW1aj9vMb+EolE488XAOjXrx/Onj2LYcOGqTS2zM3ExASdO3eGl5cX5s6dq3G/VKGnp4dx48bhwoULmDFjBurUqSM6TjM3N0fr1q2xYsUKnD59Wm4iUEJCgmB7VT/7ipJPk8c1PQcVadOmDc6cOYMJEyZoNKY0NDTEF198gbVr12LdunUq7WtmZgYvLy8sWrRI6UR+KysrzJw5E9u3b4elpaU6XS5Qbdu2xZkzZ7B8+XLUq1dPrTacnZ0xefJkXLx4EWPHjtVqYm/r1q3h7e2t9EQeiUSCTp064fjx41otbKDr833kyJE4fvw4OnfurPZ4sXbt2ipPYi6K30eUJPxulYiIiIhIeySy3OsdExERERER0X9aQEAAfv31V7lY9erVMWvWLK0eJzw8HGfPnsW///6Lx48fIzY2VnQ7Q0NDlC9fHq6urmjQoAGaNWsGNzc3lX+EKykePXqE8+fPw8/PDwEBAUhOThbdzszMDJUqVULVqlXRuHFjNGvWTGs/Pj58+BAnT57EvXv3EBISgsTERBgYGMDGxgaVK1eGu7s7unTpAldXV60cT5tiY2Px5s0bQbxmzZpFtoKfJgIDA3H06FFcv34dAQEBguXpAcDGxgYNGjRA+/bt0aVLF40Sg4cPHw4/Pz+5WO5l5T98+AAfHx+cO3cOz549E/TJ3NwcTZs2RZ8+fdC+fXutV1QDgPfv3+Ps2bO4du0aHj16hMjISNHt9PX14eLigsqVK6NevXpo1qyZwsSm4iIuLg53797F/fv38fDhQwQHByMyMhJiXxHq6emhfPnyqFGjBtq2bYuOHTtqlKCmDenp6bhw4ULONTA8PBxJSUlISUlBVlaW3LbOzs64ePGiWseJjY3FF198ITg/fXx8dJbgog0ZGRl48uQJHj9+jKCgIAQHByM8PByJiYlISkpCeno6TE1NYW5uDgsLCzg7O6N69epwc3ODh4cHHB0dddKv1NRUXLx4ERcuXIC/vz/Cw8NFtzM3N4erqysaNWqEli1bwsPDQ6Nrs1QqxalTp3KOGxcXJ9jG0NAQbm5uaNmyJXr27InKlSurfbzCUNDjqcDAQNy7dw/379/Hs2fP8ObNG9HKx8DHCv7VqlVD3bp10bVrVzRs2FDlv08XIiIisGvXLly9ehUvXrwQXP+MjY1Rq1YttGnTBv369RMkNx4+fBhSqVQu1rt37yIzsUZTn8YOt2/fRlBQEBITEyGRSGBlZYWKFSuiYcOG6NSpE2rXrl3YXdWJV69eCV5fCwsLVKlSpZB6pH2RkZHYv3+/6GMODg746quvNGo/Li4Ou3btEn3M0tISo0aN0qj93BITE3HhwgVcuXIFDx48QHh4uOi4RiKRoGzZsqhUqRJq166NZs2aoWHDhkXqHiA9PR3h4eGQSqUwNDSEjY0NnJycFF6r+/btiydPnsjF9u7dW2Sut5p48uQJzp07h4CAAAQGBiIhIQFJSUmi9zO57zeUkZqaisuXL+Off/6Bv78/QkJCkJ2dLbqto6MjKleujJo1a6Jp06Zo3LixVsbE6enpOHPmDC5fvownT54gMjISaWlpMDExQenSpVGtWjW0atUKnTp1KtafMaGhobhx4wb8/f0RFBSUc46npqbC0NAQZmZmcHJyQoUKFVC3bl14eHigTp06BfKdz71793Dp0iX4+fkhMjISsbGxyM7Ozvluw8PDA127dtX5+FDX53tYWBhOnjyJ69ev49GjR0hKShLd7tN516xZM7Ru3Vrj73QK+vuI/yJ+t0pEREREpD4mzxMREREREVGRIJVKERkZidTUVMhkspzEPltb22KdqKpr79+/R2xsLFJTU2FgYCD3vBF9LisrCxEREUhISEBGRgZMTExgb2+v1XNFmeT5z2VkZODdu3c5iXmlSpVC6dKlC/wH3JSUFERGRiI5ORlZWVkwMzODhYUFSpUqVaQSqnQlPT0dMTExOUnopqamOX+/JpXMi7Nt27Zh5cqVcrEaNWrg8OHDhdOhEiYlJQURERFITk7O+cy3srKCnZ2dTo8bGxuLmJiYnGQtKysrlC5dukSNMwpjPBUfH4+4uLica6i5uTksLS1ha2urkwlQ2pSUlITY2FjEx8fDwMAA1tbWcHJyKvL9JiLF0tPTERERgaSkJGRmZsLMzAzm5uYoVaoUjI2NC7t7WpOamopGjRohMzMzJ6avr487d+4U+mTH4igjIwORkZGQSqVykw1tbGz4fFKJ8+l7pJSUFBgZGcHc3By2trYwNzfX2TEL4vsI4nerRERERESq0N4aZ0REREREREQasLCwYIUpNdjZ2ek82ZBKBn19fTg7Oxep5c8/VT8rbKampqhYsWJhd6PQGBkZoWzZsoXdjSLFx8dHEBs0aFAh9KRkMjU1RaVKlQr8uLa2tiU+QacwxlPW1tawtrYu0GNqi7m5OczNzVGuXLnC7goRaYmRkVGRGF/q2rlz5+QS5wHA1dWVid5qMjQ0hIuLS2F3g6hAFMb3SEXx+4iSiN+tEhEREREpj+VTiIiIiIiIiIiIiP6/GzduICgoSC5mbm6OHj16FFKPiIiIiOTt2rVLEHN3dy+EnhARERERERERFT9MniciIiIiIiIiIiL6/7Zu3SqI9enThxX8iIiIqEj466+/8ODBA0F8wIABhdAbIiIiIiIiIqLih8nzRERERERERERERPhYdf7atWtyMYlEgiFDhhRSj4iIiKikOX78OLKystTed9WqVYJ4vXr1ULNmTU27RkRERERERET0n8DkeSIiIiIiIiIiIvrPe/fuHebPny+Id+nSBa6uroXQIyIiIiqJZs2ahc6dO2PHjh2IiopSap/w8HD873//w4wZM5CRkSH3mJ6eHmbMmKGLrhIRERERERERlUgGhd0BIiIiIiIiIiIiooISGxuLN2/eAACysrIQGRmJR48eYe/evUhJSZHb1sDAAJMnTy6MbhIREVEJ9ubNGyxduhTLly9H3bp1UadOHdSoUQO2trawtLREWloa4uPj8fr1a9y+fRt37txBZmamaFtff/01mjRpUsB/ARERERERERFR8cXkeSIiIiIiIiIiIvrPuHz5MubOnavUtuPHj0flypV13CMiIiL6r8rOzsb9+/dx//59tfbv3Lkzpk2bptU+ERERERERERGVdEyeJyIiIiIiIiIiIsrFw8MD3377bWF3g4iIiEhAIpHgm2++wbRp0yCRSAq7O0RERERERERExYpeYXeAiIiIiIiIiIiIqCjp3LkzvLy8YGRkVNhdISIiohJm/PjxqFixolr76unpoWvXrjh27Bi+//57Js4TEREREREREamBleeJiIiIiIiIiIjoP0sikcDMzAxlypRBw4YN0bt3bzRq1Kiwu0VEREQl1LRp0zBt2jQEBwfj3r17ePjwId68eYPw8HDExcUhJSUFmZmZsLCwgLW1NWxsbFCjRg14eHjAw8MDjo6Ohf0nEBEREREREREVaxKZTCYr7E4QEREREREREREREREREREREREREREREemSXmF3gIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhI15g8T0REREREREREREREREREREREREREREQlHpPniYiIiIiIiIiIiIiIiIiIiIiIiIiIiKjEY/I8EREREREREREREREREREREREREREREZV4TJ4nIiIiIiIiIiIiIiIiIiIiIiIiIiIiohKPyfNEREREREREREREREREREREREREREREVOIxeZ6IiIiIiIiIiIiIiIiIiIiIiIiIiIiISjwmzxMRERERERERERUwX19fVK9eXe6/OXPmFHa3iIiISpQ5c+YIPm99fX0Lu1tEJUpYWJjgfdauXbvC7hYRERERERERkUIGhd0BIiIiIiIiIiIiIiIiIvooLCwMoaGhCA8Ph1QqRUpKCgwMDGBtbQ1LS0vY2dnBzc0NVlZWhd1VIiIiIiIiIiKiYofJ80RERERERERERFTstGvXDuHh4YXdDSxbtgx9+/Yt7G4QEVExlpiYiAsXLuD8+fO4e/cuYmNj891HIpGgQoUKqFOnDjp06IB27drByMioAHpLRERERERERERUvDF5noiIiIiIiIiIiIiKHD8/P6SmpsrFPDw8YGJiUkg9ouImJCQEISEhcrEKFSqgQoUKhdQjInkRERHYunUrDh48iOTkZJX2lclkCA4ORnBwMI4dOwZLS0t06tQJY8aMQeXKlXXU45Lv4cOHiIuLk4vVrVsXNjY2hdIfIiIiIiIiIiLSPibPExEREREREREREVGRM2fOHMHqAhcuXICLi0sh9YiKm6NHj8LT01MuNmnSJEyePLmQekT0UVZWFrZt24ZNmzapnDSvSGJiIg4cOIDDhw+jX79+mDRpEhwdHbXS9n/Jr7/+Cj8/P7nYzp070aRJk0LqERERERERERERaRuT54mIiIiIiIiIiIiIiIgKQExMDL7//ntBgrYYGxsbODs7w9zcHPr6+khJSUF0dDTevXuH7Oxs0X0yMzPh7e2Nx48fw9fXV9vdJyIiIiIiIiIiKvaYPE9ERERERERERETFjqenJ9LT01Xez8fHBwcOHBDEvb291epH+fLl1dqPiIj+e8LDw/H1118jJCRE9HFjY2N07NgRX375JRo1agR7e3vR7dLS0vDw4UNcv34dJ0+eFG0vKytLq30nIiIiIiIiIiIqKZg8T0RERERERERERMVOzZo11drv6tWrovH69etr0BsiIqK8xcbGYuTIkQgNDRU8pqenhyFDhuDbb79VmDD/OWNjYzRu3BiNGzfGtGnTcPXqVXh5eeHOnTu66DoREREREREREVGJwuR5IiIiIiIiIiIiIiIiIh3JzMzEd999J5o4b29vj3Xr1sHd3V3t9lu1aoVWrVrh5MmT+OWXX/D+/XtNuktERERERERERFSi6RV2B4iIiIiIiIiIiIiIiIhKqj/++AP37t0TxB0dHbFnzx6NEuc/17VrVxw9ehQtWrTQSntEREREREREREQlEZPniYiIiIiIiIiIiIiIiHQgNDQUmzZtEsT19fWxfv16VKhQQavHs7e3x+bNmzFw4ECttktERERERERERFRSGBR2B4iIiIiIiIiIiEixzMxMPH36FIGBgYiNjUV6ejrMzc1Rrlw51KtXD7a2toXdRcqDVCrF06dPERISgvj4eKSnp8PMzAwuLi7o0KGDSm2lp6cjICAAISEheP/+PVJSUmBmZgZ7e3s4Ojqibt26MDY21tFfAiQmJuL169cICQlBQkICkpKSoK+vD2tra9jY2KBSpUpwdXWFRCLRWR8KWkhICJ49e4aIiAikpKTA1NQUZcqUQf369eHk5KRSWxkZGQgICMDLly/x4cMHZGVlwdbWFs7OzmjUqBGMjIx09Fd8lJWVhRcvXiAoKAhRUVE5f4+trS0cHBxQp04dWFhY6LQPuSUlJeHRo0cIDg5GQkIC9PX1UapUKZQuXRoNGzaEiYlJgfanqIiNjcXr168RFhaW814zNjaGtbU1rK2tUb16dZQrV65Q+hYdHY0nT54gNDQUUqkUZmZmKFWqFCpWrIjatWtDT093NZtiY2Px4MEDvHnzJuc5sbW1haurK2rWrAkDg6L5k9e6deuQkZEhiI8dOxYNGjTQyTENDAwwdOhQjdsJCgrCy5cvERERgeTkZEgkEpibm6Ns2bKoXr06nJ2dtdBb1RTmOVhQPnz4gGfPnuHNmzdISEhAZmYmzM3NUa1aNTRr1kzpdjIzMxEQEICgoCDExMQgJSUFhoaGsLKyQrly5VCrVi1YW1vr8C8hIiIiIiIiIiqaiuY3iURERERERERERP9xoaGh2LZtG06ePIn4+HjRbSQSCRo3boxRo0ahffv2Sre9d+9eLFy4UC5Wvnx5nD17ViuJz6tWrcKWLVvkYh4eHti1a5fGbRcF7dq1Q3h4uFzswoULcHFxAQDIZDKcP38ee/bswe3bt0WTJp2dnZVKns/KysKZM2dw+PBh3LhxA+np6Qq3NTExQbNmzdC7d2907txZxb9KKDExEZcvX8bNmzdx69YthIaG5rtPqVKl4OHhgaFDh6JJkyYqHW/Dhg3w9PTMcxtlz/NJkyZh8uTJgnhYWJigDWdnZ1y8eDHn3ykpKdi7dy+8vb0RHBys8BgeHh6YOHFivomM4eHh2LJlC06dOoW4uDjRbczNzdGpUydMnz4dDg4OebanCplMhitXrsDX1xdXrlxBcnKywm0NDQ3RqFEjdO/eHX379oW+vr5ax8zv/QEA/v7+2LZtG/755x+F57SxsTGaNWuG7777DnXr1lXq2GKvb26enp75nmeA8LzQpZiYGFy6dAm3bt3CrVu3EBUVle8+jo6OaNasGUaNGoWaNWuqfWxl3hPZ2dk4efIkdu3ahQcPHkAmk4m2ZWNjg06dOuG7775TeXJJXs6fP48dO3bg9u3bCo9tbW2Nbt26YfTo0YU2sUBMZGQkTp06JYg7Oztj0qRJhdCj/AUGBmL37t04c+YMYmJi8tzWxcUF3bp1w9ChQ9V+zQv7HPT19cXcuXPz3GbEiBFKtdWnTx8sX75c9LHq1asLYs+fP8/5/8zMTBw7dgze3t548OABsrOzBdt7eHgolTx//fp17N+/H//88w9SUlIUbieRSFC7dm306tUL/fv3h6mpab5tExERERERERGVBEyeJyIiIiIiIiIiKmJ27NiBNWvWIDU1Nc/tZDIZ/Pz84Ofnh5YtW2L58uVKJd727NkTv/76K5KSknJib968wb///osWLVpo1PeMjAwcOnRIEB80aJBG7RYXYWFhmDVrFu7evatxWxcvXsTSpUuVSloHgNTUVFy6dAmXLl1CgwYNMH/+fNSpU0fl4z5//hy//fYbrl69Kpr4n5cPHz7gzJkzOHPmDBo0aIAlS5bA1dVV5T4Ulnv37uGHH35AWFhYvtt+eu8NGzYM8+bNEySby2Qy/Pnnn1izZk2+z2NSUhJ8fX1x9uxZrFy5UqXJMIrcvXsXixcvxrNnz5TaPiMjAzdv3sTNmzfx119/Yd68eRpfD3JLSkrC4sWLRa8RuaWlpeHy5cv4559/MHDgQCxYsEDthP6iys/PD7///jv8/PyQlZWl0r5RUVE4cuQIjhw5gtatW2Px4sVaTVj/JCQkBLNmzcL9+/fz3TYuLg7e3t44cuQI5s+fj4EDB2p07KioKMyZMwfXr1/Pd9v4+Hjs2bMHvr6++P777zFq1CiNjq0tBw8eRGZmpiA+YMAAna82oaqEhASsWLECvr6+oonbYsLCwvDHH39g+/btGDNmDL777jut/12FeQ4WlKdPn2L27Nl48eKFRu0EBgbip59+wp07d5TaXiaT4dGjR3j06BE2bdqE2bNno1evXhr1gYiIiIiIiIioOCj+axcSERERERERERGVIEuWLMHSpUvzTZzP7dq1axgwYABev36d77bm5uaiyVHe3t4qHVPMhQsXBJVqS5UqhS+//FLjtou6p0+fon///honzqenp+Onn37Ct99+q3TifG7+/v4YMmQIzp07p/K+Dx8+xMWLF1VOnBfrw8CBA3Hp0iWN2ikoFy9exMiRI5VKnP/c33//jR9//FEulp2djblz52LFihUqPY9SqRRTp07F5cuXVerD52QyGTw9PTFs2DClE+dze/XqFcaOHYvdu3er3Y/c3r9/j8GDByuVOP85mUwGb29vTJkyReUE86Lu+vXruHHjhsZ/1z///IN+/foplVysinv37mHgwIEqt5uamor//e9/2L59u9rHDgwMxIABA5RKnM997GXLluGXX35R+9jaJHYNNjQ0RP/+/QuhN4q9evUKffv2xYEDB5ROnP9cRkYGvLy8MGTIEKVWTlBWYZ6DBeXq1asYMmSIxonzZ86cQf/+/ZVOnM/t/fv3mDVrFubOnSs64YOIiIiIiIiIqCRh5XkiIiIiIiIiIqIi4q+//sLOnTsFcScnJzg5OcHIyAhRUVEICwsTTW579+4dRo8eDW9v73wrEA8ePBh79uyRi128eBExMTGwt7dX+2/Yv3+/INanT58iV2FX2yIiIjB16lR8+PBBLm5sbAxnZ2fY2dkhNTUVkZGRiI6OVthOeno6Jk2ahH/++UfhNhYWFnB2doaNjQ3S0tIQGRmJd+/eibY1depULF++HD179lT/j/uMjY0NHBwcYGFhAVNTU0ilUrx//x7h4eGi20ulUkyePBl79uxB3bp1tdIHXXj48CG+//57pKeny8XLlCkDR0dHGBsbIyYmBkFBQZDJZIL9fX190bBhQwwYMAAARKurGxoaonz58rCzs0NGRgbCwsJEz4WMjAzMnj0bp06dgq2trUp/h0wmw//+9z/4+Pgo3MbU1BQuLi6wsbFBZmYmYmJiRCdpZGdnY9GiRUhKSsL48eNV6kduycnJ+O677/D8+XO5uLGxMcqVKwdbW1tkZGQgIiJC9FwGgPPnz2P79u0YN26cRn0pLuzs7GBnZwcLCwsYGxtDKpUiMjJSYWJydHQ0xo0bh0OHDsHFxUXj47948QLjx49HYmKiXNza2hplypSBtbU1kpKSEBYWhri4ONE2fv31VzRs2BD169dX6diRkZEYPXo0IiIiRB/X19eHs7MzHB0dkZ6ejoiICMHzsmvXLjg7O6t0XG2Ljo7G06dPBfHGjRsrtUpMQXn9+jWGDx+O2NhY0cclEknO852dna3wMwcAHj16hBEjRmDPnj0qX79yK8xzsKAEBARgypQpSElJkYubmZnB2dkZpUqVQlJSEiIjIwUTEz936tQpzJgxQ+FEHCMjIzg7O8PBwQFSqRRv375V+Jz5+voiKSkJ69atg0QiUftvIyIiIiIiIiIqypg8T0REREREREREVAS8fv0aJ06cyPm3vr4+hgwZgkGDBqFq1apy20ZHR+P48eP4/fffkZCQIPfYu3fvMH/+fGzdujXP41WrVg2NGjWSq5KekZEBX19ftRNlQ0ND8e+//wriAwcOVKu94mThwoVyiW2NGzfG2LFj0bRpU5iYmMhtGx0drbCy+NKlS0UT501MTNCvXz/07dsXNWrUgL6+vtzjwcHBOHDgAHbu3Im0tLSceFZWFn766SfUrl0blStXVvnvcnZ2Rrt27dCqVSu4ubkpnJQRHx+Pq1evYs+ePYLK+xkZGfj+++9x5MgRWFhYKDzWgAED0KpVq5x/T5o0SZBc7unpqVTSaenSpfPd5pO0tDTMmDEjZ7UHU1NTjBo1Cn379kX58uXlto2Ojsaff/6JHTt2CCrzrlq1Cl26dMHFixflJqa4urpi4sSJaNOmjeDvf/DgAVasWCF4zuLi4rBmzRqVq2dv3bpVNHHewMAAXbt2xaBBg1C3bl3BZJaIiAgcPXoUmzdvFiSqrl27Fg0aNEDjxo1V6svnFi1ahICAgJx/N2nSBGPGjEGTJk0E74/Xr1/jjz/+wOHDhwXtbNiwAd26dUPZsmVFj+Po6Ci3goaPjw8OHDggt03//v1zJjnkpaAn/Li6uqJ9+/Zo3rw5qlevrjDx+P379zh//jx2794tmIyQkJCA6dOnY8+ePTAwUP/nn7S0NEyZMiXnXNDX10efPn0waNAg1K5dG3p6/7eocXZ2Nu7cuYM1a9bA399frp3s7GzMnz8fx48fVykJd/78+aKJ89bW1pg4cSK6d+8umOT1/Plz7N+/H3v27MmZXLZmzRrUqFFD6eNq24MHD0TjRWkiUXp6OqZNmyaaOG9ubo4JEyagZ8+egmtqUFAQDh48iL/++kuwukZQUBDmzZsHLy8vtftVkOdgmzZt5K4bP//8s2DSw08//YRatWrl229VJwzMnDkTycnJOf9u3749RowYgUaNGsHQ0FBu29DQUMHfBwAhISGYN2+eaOJ8hQoVMHHiRHTo0EHu80cmk+Hu3bvYuXMnzpw5I9jvzJkz2L59O8aMGaPS30NEREREREREVFxIZGJlcoiIiIiIiIiIiEhnfH19MXfuXIWPlypVCps3b843wS4yMhKTJk3Cw4cPBY+tXLkSvXr1ynP/Y8eOYebMmXKx8uXL4+zZs2pVG129ejU2b94sF2vSpIloNf3CsmHDBnh6egriuZNQ89KuXTuFldb19fWxYMECDBo0SOW+nTlzBlOmTBHE69Wrh99++01hwvDnAgMDMW7cOEH/atWqBR8fH0HSvZiDBw/i4sWL+Prrr+Hu7q78H/DZ/gsWLBAkVM6cOVOlquFiz/OFCxc0qqodFhaG9u3bK3y8UqVK2LJlC8qVK5dnOydPnsT06dMFVegnTZqEv//+O6ei7/DhwzF37tw8n/fMzExMmjQJly5dkoubmJjg2rVrsLS0zOev+uj+/fsYOnSoIKm/YsWKWL9+PapXr55vG1FRUZgwYQKePHkiFy9btiyOHTuW5+SHT/J6fxgaGuKXX35B7969821n7969WLhwoSA+ceJETJ06Nd/9AfH3+6RJkzB58mSl9te1jRs34s2bN/j666/h5uam0r5ZWVnYunUr1q5dKzgP165di65duyrVTn7vCScnJ2zatCnfxOGsrCzMnj0bx44dEzz2119/oVmzZkr15/Dhw5g9e7YgXq9ePXh6esLR0THP/f39/TFhwgSFVbUBYNmyZejbt69S/dHE+vXrsXHjRkF848aN6NChg86Pr4zly5fjzz//FMRr1aqFjRs3okyZMnnuHxAQgG+//RZv374VPLZgwQIMGTIk3z4UtXNw+PDh8PPzk4vt3LkTTZo0UWp/RfK6BpuYmGDt2rVo166dSm1mZmZiyJAhohM1+vbtiwULFggmKOV28uRJzJo1S/CZbWhoiAMHDih1bRJ7DZ2dnXHx4kUl/goiIiIiIiIiooKnl/8mREREREREREREVFDMzMywdetWpSrTOjk5YcuWLXB1dRU89uuvvwoSoXLr1KmToErqmzdvcOPGDdU6jY8JXIcOHRLEv/rqK5XbKs6WLFmiVuK8VCrF//73P0Hcw8MDf//9t1KJ88DH6tV79+4VvK5PnjzBhQsXlGqjb9++2Lhxo1qJ8wDQr18/rF69WhDfs2ePaGXcoqJ06dLYs2dPvonzANC1a1f07NlTEPf09MxJ2h0yZAh+/PHHfCcsGBgYYOnSpYIk+dTUVJw+fVqpvmdnZ2P27NmCxHlXV1f4+PgolTgPfKzcvnPnTlSsWFEu/vbtW9GK9qqQSCRYv369UonzADB48GB0795dEPf19dWoH0XJxIkTsWLFCpUT54GPE3W++eYbzJo1S/DYrl27tNE92NjYYO/evUpV3NbX18eSJUsEqzUAyr9mGRkZ+PXXXwVxV1dXbN68Od/EeQBo0KABNm/eDFNTU6WOqUuhoaGi8cKshv+50NBQ7NixQxCvXLkytm3blm/iPAC4ublh27ZtKFWqlOCxNWvWyFVVV0dBn4OFQU9PD15eXionzgMfJ0GKJc537doVS5YsyTdx/tO2q1atkqvmD3x8Py5fvlzlPhERERERERERFQdMniciIiIiIiIiIipCJk6ciNq1ayu9vY2NDZYsWSKoFB8dHY3z58/nua+RkRH69esniHt7eyt9/E8uXLiA6OhouZitrW2Rqa5bEDp27Ig+ffqota+Pjw/i4+PlYg4ODtiwYQOMjIxUasvJyQmLFy8WxMWSJMWos+pAbp06dRK89m/fvsWdO3c0bltXli9fLph0kJdRo0YpfKxSpUqYM2eO0m3Z2tqKJpUr+3ydP38ewcHBcjETExN4eXnByspK6X4AgIWFBdasWSM4D3bt2qXR5IcRI0aonBwqViE+IiICYWFhavejKNHGe23UqFGCxOJ79+5p5TlasmQJnJ2dld7e2NhYdHWJu3fvKrX/uXPnEBMTIxeTSCRYunQpbGxslO5HvXr18M033yi9va5ERESIxsUSzQvD7t27kZ2dLRfT09PDihUrVOpj5cqVMX/+fEE8MTFRdFKdKgr6HCwMI0eOVLoqfm5iE2UcHR2xePFiQTJ8Xjp37oz+/fsL4jdu3MCrV6/U6hsRERERERERUVHG5HkiIiIiIiIiIqIiokyZMhg5cqTK+zVo0ACdO3cWxJVJgv/qq68ECVYXLlzA+/fvVeqD2LH69OmjcuJ3cTZlyhS19svMzBRNbJ80aZJKCaOf69Chg6Da+J07dwQJ1rokNpHg/v37BXZ8VTRs2FDl5MWaNWsqrMw8duxYGBsbq9Re+/btBbGnT58qte/27dsFsREjRohWYFZGrVq10KZNG7lYeHg4bt26pVZ7JiYmmDBhgsr7VaxYUbRK95MnT9TqR0mkp6eHXr16CeKavtdq166t1uSnzp07CyYFhIeH48OHD/nuu3//fkGsS5cuqF+/vsr9GDNmDJycnFTeT5sSEhIEMUNDQ5iZmRVCb+SlpaXh4MGDgnjv3r2VWvkmtx49eqBBgwaC+O7du9XqH1A452BBMzIyUnuix/3790WvhdOnT4eFhYXK7U2fPh3m5uaC+N9//61W/4iIiIiIiIiIijImzxMRERERERERERURPXv2VDvZXKyC/O3bt5Gamprnfi4uLmjVqpVcLCMjQzSpTpHQ0FD8+++/cjGJRIKBAwcq3UZxV7duXVSrVk2tfe/du4d3797JxczNzdG3b1+N+tSzZ09BrCCr74olYBbV5Hmx948yxF5zIyMjdO/eXeW2ck92AICgoKB893v79i38/f3lYhKJBMOGDVO5D58TS8hWd+WA9u3bq1TV/3NiK3EU5CSQ4kAX77UBAwaotZ+VlZXopI38XrOUlBTcvn1bEFf3vWlkZCR6DSxIYp+/6iQ168KDBw9Ek/s1+dz+6quvBLHAwEDB55uyCvocLAzt27dXeyWCa9euCWKWlpbo2rWrWu2VKlUKnTp1Uuo4RERERERERETFHZPniYiIiIiIiIiIigix6vHKat68OaytreVimZmZSlVoHjx4sCDm4+MDmUym1LHFtm3atCkqVqyo1P4lQZMmTdTeVyxhtE2bNhpX7Xd3dxfE7t27p1GbqrCzsxPEQkNDC+z4qhB7rpTh7OwsiNWqVQsmJiYqt2VrawtTU1O5WEZGBtLS0vLcz8/PTxCrV6+exlW3tXn+NG7cWO1+iCXBJiYmqt1eSaSL91pBv2ZPnjxBZmamXMza2lrlFSE+16VLF7X31YasrCxBrKisxiI2ucLZ2Vm0eryyvvzyS9G/L/fkHmX9F64bmowdxJ7XDh06qLzqyefEJpyEhoYiJiZG7TaJiIiIiIiIiIoig8LuABEREREREREREX1MqFO3cjkA6Ovrw83NDbdu3ZKLP3z4EI0aNcpz39atW8PZ2Rnh4eE5sTdv3uDmzZv5Ji5mZmbC19dXEP8vVZ0HPiZMq0usGrxYtW1ViSV2P3/+XK22QkJCcO3aNTx//hwvXrxATEwMpFIpkpKSkJ6ernQ7YpWOC5uBgYHaEz3Mzc0FMVdXV7X7Ym5ujpSUFLlYYmJinsmQujp/HBwcYGxsLJe8r+75U7VqVbX7IVapuygmwWpLQEAAbt68iRcvXuDly5d4//49kpKSkJSUhIyMDKXb0eS9pq+vj8qVK6u9v9hrJpVK89zn4cOHgliNGjWgr6+vdj+qVasGQ0NDlZ43bRJ73+b3PBQUseT5evXqadSmmZkZqlWrhsePHwuOpWo19MI4BwuDumMHmUwm+p7R9DWsU6cOJBKJYELk/fv30aFDB43aJiIiIiIiIiIqSpg8T0REREREREREVARUqVIFBgaafV1XvXp1QfL85wnxiujp6WHgwIFYu3atXHzfvn35Js9fuHAB0dHRcjE7O7v/XJJV6dKl1d73xYsXglhWVpZocqMqcldxBoD4+Hil95fJZDhy5Ah2794tmqSnjqKY9GxlZaX2vmLJsblXgNC0vfwqz798+VIQ09fX1/j8AQBTU1O546ty/nxOk+dErIq/KhM2ioOMjAzs2bMHPj4+oq+nOjR5r1lZWUEikai9v9hrlt95LPZZ5ebmpnYfAMDQ0BCurq4ICAjQqB115V5JAgCSkpKQlZWl0aQAbRB7vmvUqKFxuzVr1hQkz799+1bldgrjHCwM6o4dEhMTRSfIaPoaWlhYoEKFCggODpaLq/MaEhEREREREREVZUyeJyIiIiIiIiIiKgJsbW01bsPOzk4QU7b6cP/+/eHp6SlXoffChQt4//69aLuf7N+/XxDr27cvjIyMlDpuSWFpaan2vmIJyatWrdKkOyodS0xQUBDmzp0Lf39/rR4/OTlZq+1pg1iCa1FqL3cF4Nw+fPggiO3YsQM7duzQaj+Aj5M6pFKpaFXnvBT0c1KcPHjwAPPmzcOrV6+02q4m7zUzMzMt9uSj/F4zsWtTXp89ytJGG+qyt7cXjScmJsLGxqZgO5OL2NjA0dFR43bF2lBn0k1hnIOFQd2xg6LnVFuvYe7k+aK4agwRERERERERkSb0CrsDREREREREREREBJWTUcWIJWEpm/Bkb2+Pjh07ysUyMjLg6+urcJ+wsDBcv35dLiaRSDBw4ECljlmSiFW5VUZSUlKBVtFWphr1y5cvMWzYMK0nzpNuxMXFFejxiuLqAcWVn58fRo0apfXE+eJI7LwyNzfXuF1tfLaqq0yZMqLxsLCwAu6JkFjydWGPQ/6L1J1YpOg51cZrKNaGuquOEBEREREREREVVaw8T0REREREREREVAQYGhpq3IZYtXdVErMHDx6MkydPysV8fHwwduxYSCQSwfY+Pj6CSq7NmjVD+fLllT7mf51UKi3Q42VnZ+f5eHJyMsaNG4eYmBjRxx0cHNCwYUNUqVIFZcqUgZ2dHYyNjWFsbAwDA+HXzYMGDdJKv0mxgj6HsrKyCvR4JVVkZCQmTJigsEK8s7MzGjZsiEqVKqF06dKws7ODkZERjI2Noa+vL7dtdHQ0Jk2aVBDd1hmxzyptrGBSmKugVKlSRTT+6NEj1K5du4B7Iy8tLU0QMzY21rhdsTZSUlI0bpfkpaamisbVnciXXxt8DYmIiIiIiIiopGHyPBERERERERERURGQlJSkcRtiSbRiVWAV8fDwQJUqVeSqIIeEhODmzZto1qyZ3LaZmZk4ePCgoA0mS6tGT69oLQ7q5eWFd+/eCeKNGzfG5MmT0aRJE6XbyszM1GbXSIGidg6RclauXCl63e/QoQMmTpyIWrVqKd1WSEiINrtWKMQ+q3T1uVhQ6tSpIxp/+PAhBg8eXMC9kWdhYSFYtUIbz7dYG4VZ/b+kUvScSqVS2NraatS22GuoyliSiIiIiIiIiKg4YPI8ERERERERERFREaCNBL/ExERBzMrKSqU2Bg8ejMWLF8vFvL29BcnzFy9eRHR0tFzM3t4e7du3V+l4/3Xm5uai8TNnzqBixYoF2pfMzEzs27dPEO/evTtWrlwpqHadn4SEBG11jfJgbm4uqNq9fft2tGjRopB6RPmJjY0VrPIBAOPGjcPMmTNVbi8+Pl4b3SpUYsm5Yp9pqirM5PmaNWvC0tJS8Hfcvn0bMplMdEWXgmJlZSVIntfVOMTGxkbjdkmetbW1aDwpKUnj5Hmx11DR8YiIiIiIiIiIiiuWpCEiIiIiIiIiIioCgoODddKGqklrvXv3hpmZmVzs/PnziI2NlYt5e3sL9u3Xrx8MDQ1VOt5/nZmZmWhS2ps3bwq8L3fu3BEk4drY2ODnn39WOXEeAD58+KCtrlEeypQpI4gVxvlDyrt06RKys7PlYlWqVMH333+vVnu5k6CLo1KlSgliuvpcLCgGBgZo1aqVIB4aGoobN24UQo/+j9jEutDQUI3bFWtD1Ul8lD9FzylfQyIiIiIiIiIi5TB5noiIiIiIiIiIqAiIiooSJKir6tmzZ4JYjRo1VGrDwsIC3bt3l4tlZGTg4MGDOf8OCwvDv//+K7eNRCLBgAEDVDoWfeTq6iqIBQQEFHg/njx5Ioh17NgRFhYWWmuPtK+onD+kPLH3Rvfu3dWapKKoveLGzc1NENP0PH7//r1ghZSC1qtXL9G42CofBalKlSqCmNgYQlVPnz5V6likGTMzM5QtW1YQF3v+VREdHS36nuFrSEREREREREQlDZPniYiIiIiIiIiIiojcCemqiIyMFK2wW6dOHZXbGjx4sCB24MAByGQyAICPj4+ganKLFi1Qrlw5lY9FQP369QWxixcvFng/YmJiBDGxxGxl3b17V5PukJIaNGggiF2+fDnn/fpfJpFICrsLovheE6pXr54g9vr1a0RGRqrdpiafqdryxRdfwNnZWRC/cOGCxonOmhD73Ll//z7S09PVbjM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       "text/plain": [
        "<Figure size 1500x1500 with 3 Axes>"
       ]
@@ -11096,19 +11344,30 @@
     }
    ],
    "source": [
+    "ps = idata_expend_bart[\"posterior\"][\"p\"].mean(dim=(\"chain\", \"draw\")).values\n",
     "make_plot(\n",
     "    X,\n",
-    "    idata_expend,\n",
-    "    ylims=[(-100, 260), (-40, 270), (-130, 400)],\n",
-    "    lower_bins=np.arange(6, 45, 1),\n",
+    "    idata_expend_bart,\n",
+    "    ylims=[(-100, 340), (-70, 380), (-130, 420)],\n",
+    "    lower_bins=np.arange(6, 15, 1),\n",
     "    text_pos=(11, 200),\n",
-    "    robust=False,\n",
+    "    method=\"robust\",\n",
+    "    ps=ps,\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a __disastrous__ result. Evaluated at the expected values of the posterior propensity score distribution the robust IPW estimator of ATE suggests a substantial difference in the treatment and control groups. What is going on?\n",
+    "\n",
+    "What happens if we look at the posterior ATE distributions under different estimators?"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 616,
+   "execution_count": 817,
    "metadata": {},
    "outputs": [
     {
@@ -11140,33 +11399,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>-0.185291</td>\n",
-       "      <td>8.167003</td>\n",
-       "      <td>7.981713</td>\n",
+       "      <td>0.031908</td>\n",
+       "      <td>8.035200</td>\n",
+       "      <td>8.067108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.000277</td>\n",
-       "      <td>8.008821</td>\n",
-       "      <td>8.009098</td>\n",
+       "      <td>0.017296</td>\n",
+       "      <td>8.048349</td>\n",
+       "      <td>8.065646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.430608</td>\n",
-       "      <td>8.333385</td>\n",
-       "      <td>7.902776</td>\n",
+       "      <td>-0.015539</td>\n",
+       "      <td>8.075015</td>\n",
+       "      <td>8.059477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>-0.012320</td>\n",
-       "      <td>8.031375</td>\n",
-       "      <td>8.019055</td>\n",
+       "      <td>-0.016085</td>\n",
+       "      <td>8.077059</td>\n",
+       "      <td>8.060974</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>-0.443388</td>\n",
-       "      <td>8.331288</td>\n",
-       "      <td>7.887900</td>\n",
+       "      <td>-0.057802</td>\n",
+       "      <td>8.114834</td>\n",
+       "      <td>8.057032</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -11174,33 +11433,69 @@
       ],
       "text/plain": [
        "        ATE   E(Y(1))   E(Y(0))\n",
-       "0 -0.185291  8.167003  7.981713\n",
-       "1  0.000277  8.008821  8.009098\n",
-       "2 -0.430608  8.333385  7.902776\n",
-       "3 -0.012320  8.031375  8.019055\n",
-       "4 -0.443388  8.331288  7.887900"
+       "0  0.031908  8.035200  8.067108\n",
+       "1  0.017296  8.048349  8.065646\n",
+       "2 -0.015539  8.075015  8.059477\n",
+       "3 -0.016085  8.077059  8.060974\n",
+       "4 -0.057802  8.114834  8.057032"
       ]
      },
-     "execution_count": 616,
+     "execution_count": 817,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "qs = range(4000)\n",
-    "ate_dist = [get_ate(X, t, y, q, idata_expend, robust=True) for q in qs]\n",
-    "ate_dist_df = pd.DataFrame(ate_dist, columns=[\"ATE\", \"E(Y(1))\", \"E(Y(0))\"])\n",
-    "ate_dist_df.head()"
+    "ate_dist = [get_ate(X, t, y, q, idata_expend_bart, method=\"doubly_robust\") for q in qs]\n",
+    "ate_dist_df_dr = pd.DataFrame(ate_dist, columns=[\"ATE\", \"E(Y(1))\", \"E(Y(0))\"])\n",
+    "\n",
+    "ate_dist = [get_ate(X, t, y, q, idata_expend_bart, method=\"robust\") for q in qs]\n",
+    "ate_dist_df_r = pd.DataFrame(ate_dist, columns=[\"ATE\", \"E(Y(1))\", \"E(Y(0))\"])\n",
+    "\n",
+    "ate_dist_df_dr.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 818,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 711,
+       "width": 2011
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_ate(ate_dist_df_r)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Deriving ATE estimates across draws from the posterior distribution and averaging these seems to give a more sensible figure, but still inflated. If instead we use the doubly robust estimator we recover a much more sensible figure. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 617,
+   "execution_count": 819,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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urnr06KHMzEzV1dXp0KFD2rBhg8rLy019x48frwcffDCgMQEA/BMuNeW0/v37q6ysrMl+LG4HAGusW7dOkyZNOuNBiJdeeqkyMjKUn58f1AchBmNOKgDAGuFUU4I1dxQAIgGL25vh66+/1vPPP69169Z5fYpK+/btNWrUKE2aNEnx8fEBP/8///lPTZkyxWjn5eVp8eLFATl2XV2dbrrpJu3cudPY1q9fP/32t789Y6FvcXGxZs6caXqK5vXXX6/Zs2c3eZ7du3frxhtvNCYCxcfH6+mnn9ZPfvITU79vvvlGd955p7788ktj24QJEzRt2rRmjQ/wJhJyqv6k1ilTpujuu+8OQPTAd6qrq3XttdfqyJEjxra8vDw999xzOvvss8/ov3HjRj344IM6fvy4sW3MmDGaMWNGQGMaNmyYjh49amwbN26c7r//flPtLS8v16OPPqqVK1ca2/r166c333yzyXNQq2ClaM0r6hWsEo455e0CZUJCgnr27KnDhw/r5MmTxvbmXqCkVsFK0ZpX1CpYJdxy6vQi3MzMTI0YMUIjR47Uueeee0Y/t9utVatW6bHHHjO9wfeaa67RH/7whybPQ62ClaI1r6hVsEq45VRRUZHuuOMODRs2TJdffrl69OjR4FvaTp06pd/+9rd69913jW02m02LFy9W//79Gz0PtQoIjVDfbwcA+G7x4sX6zW9+Y7Szs7P1xz/+8Yw3E65fv1733nuvaYHgyy+/rMsvvzxgsdRf3N63b1+NGjVK11xzjdcJyGVlZXrhhRf0+uuvm7b/8Y9/1I9+9KOAxQQA8F041ZTTvC1uz8zMVLdu3bRp0yZjG4vbASDwjh07pmHDhhkvBbPZbJo+fbrGjBljerDvqVOndO+995r+XfZ1brU/gjF3DgBgjXCrKcGY4wYAkcL3V3rA0K1bN82bN0+ffvqp5s+fr8cee0zTpk3TrFmztHjxYq1bt0733HOPZTfa60/Okb67cBYoK1euNC3C7d69uxYuXOj1DdYZGRmaPXu26abPBx98oO3btzd5nhdeeMH0hovf/OY3Z0wUkqQuXbrof/7nf9SuXTtj22uvvaZjx475PCagKZGQU4CV1qxZY5rU2rVrV7300kteJ7VK0sCBA/XKK68oLi7O2PbOO+/I4XAELKbFixebLiKOHDlS06dPP6P2pqam6vnnnze95Wnbtm366KOPmjwHtQpWita8AqwSjjkVFxennj176qabbtITTzyh9957T1u3btXbb7+t7t27B+Qc1CpYKVrzCrBKuOVU27Zt9fDDD2vt2rV64IEHvC7Alb674Tds2DC9+uqrSkpKMravWrWKa4AIuWjNK8Aq4ZZTZ511lt59911NmDBBPXv2bHBhu/Rd/s2aNUsjR440trndbi1atKjJ81CrgNAI9f12AIBvKisrNX/+fKOdkJCgRYsWnbEIUZIuu+wyzZ8/3/S9bfbs2Qr0O1f69eunxYsX66233tLIkSMbfLNWmzZt9Nhjj2nChAmm7b/73e8CGg8AwDfhWFMkKS0tTQMHDtSECRM0d+5crV27VuvXr9esWbMCfi4AgNmCBQuMRYiSdPfdd2vs2LGmRYjSd9d/X3nlFdOcgJUrV2rXrl0BjYe5cwDQeoVbTWGOGwD4jsXtLXDWWWdp6NChuvXWWzVhwgT97Gc/U15enmJjYy0756lTp/Tpp58a7eTkZA0bNixgx/d8YvEjjzyi5OTkBvvHxMRo5syZpslL8+bNa/Qcu3bt0rp164x2Xl6e8WRlb9q2baupU6ca7erqai1cuLDRcwC+ioScAqy2ceNGU/v2229v9O+xJPXs2VNDhw412hUVFdqxY0dA4nE6nXrllVeMdps2bTR9+vQG+8fExOi///u/Tb+g1r9h5g21ClaLxrwCrBRuOSVJ06dP17Jly/Tkk0/q5ptvVm5uruk7XktRq2C1aMwrwErhllMvv/yyxo4d6/Niod69e2vMmDGmbatWrWp0H2oVrBaNeQVYKdxyqjkeeOAB0z26zz77rNH+1Cog9EJxvx0A4Lu3337b9Cap8ePHN/ggL0m65JJL9NOf/tRo7969W2vXrg1YPOeff77efPNN5eXl+bzPPffco86dOxvtr776Svv37w9YTAAA34RbTTltzZo1WrRokaZNm6arr766wYf8AQAC68SJE1qyZInRzsnJOePBVPUlJCTo0UcfNdput1sLFiwIWDzMnQOA1ivcaorEHDcA8AeL21uZ5cuXy+l0Gu2rr75aKSkpATl2eXm56c0wnTp10g9+8IMm98vOztagQYOM9vr161VaWtpg/w8//NDUvuWWW5o8x09+8hOlpaU1eAyguSIhpwCrFRYWmtp9+/b1ab/vf//7pnag3mL0+eefq6ioyGj/+Mc/Vnp6eqP7nHPOOab827lzp7755psG+1OrYLVozCvASuGWU8FArYLVojGvACuFW04156bZ9ddfb2o39YZpahWsFo15BVgp3HKqOdq2batu3boZ7aqqKtP1Dk/UKgAAgMbV/64TGxurm2++ucl9PL9TBfL7UnN+77Lb7br66qtN2/jdCwCCL9xqCgAgtFavXm2auz1q1CjZ7fZG9xk4cKDp+u+6detUWVkZkHiYOwcArVe41RQAgH9Y3N7KvPfee6b2iBEjAnbsPXv2qK6uzmj7OnFJMk9ecjqdjT4lc82aNcbPdrvd9FaPhiQkJOiKK64w2kePHtWuXbt8jg9oSCTkFGA1l8tlaicmJvq0X1JSkqlts9kCEk/9OiJJ11xzjU/7DRs2zNRevXq1T+egVsEK0ZhXgJXCLaeCgVoFq0VjXgFWioScOuecc0zt+m/Z8YZaBatFY14BVoqEnJKk1NRUU9vtdjfYl1oFAADQsJMnT+rf//630e7Xr586dOjQ5H59+/ZVdna20V63bp1qa2stidFXOTk5pvaJEydCFAkARKdIqikAgMDwnKvmOQetIfXntNXU1Gj9+vWWxMPcOQBoPcKtpgAA/MPi9lZk79692r17t9Hu1KmTLr300oAd3/PtFb5cQDwtKyvL1N68ebPXfiUlJdq3b5/Rzs3NVUJCgk/n6Nevn0/nAHwVCTkFBEPnzp1N7aNHj/q037fffmtqe07Ybq4tW7YYP8fGxqpPnz4+7ef5FilqFUIp2vIKsFq45ZTVqFUIhmjLK8BqkZBTFRUVpnZjb0ujViEYoi2vAKtFQk5J0pEjR4yfU1NTlZGR4bUftQoAAKBxW7duNT0AyfM7UGPq9y0uLtZXX30V0Nj85fm7V1Nv7wIABFYk1RQAQGD861//Mn7OzMxUly5dfNrPqmuzzJ0DgNYr3GoKAMA/LG5vRTzfMH3DDTcE9A0Y1dXVpnZ8fLzP+3pO+GnoIuL+/ftN7V69evl8jtzc3EaPBfgrEnIKCIZBgwaZ2n//+9+b3Keurk6rVq0y2p06dVKPHj1aHIvL5dLBgweN9jnnnKOUlBSf9u3WrZvpjVMN1RFqFYIh2vIKsFo45VQwUKsQDNGWV4DVIiGn9u7da2rXf2OOJ2oVgiHa8gqwWiTk1KZNm1RYWGi0Bw8erJgY77dCqVUAAACN8/yO4/kdqDHh9n3J83cvf15MAABouUiqKQCAlissLFRZWZnRDvW1WebOAUDrFW41BQDgP14D0krU1tZq+fLlpm0jRowI6DnS0tJM7dLSUp/39ez79ddfe+3nuf3ss8/2+RwdO3Y0tQ8cOODzvoCnSMkpbz7//HPt2bNHe/bs0alTpxQXF6ezzjpL55xzjvLy8jRs2DDl5OT4fDzgiiuuUI8ePYyb/n/72980ZMgQ/ehHP/La3+1267nnnjNd8LvrrrsanEjqj2+//VY1NTVG2586YrPZlJ2dbcT1zTffyOl0nvFkfmoVgiHa8sob6hUCKZxyKhioVQiGaMsrb6hVCKRIyKn333/f1B4wYECDfalVCIZoyytvqFUIpNaeU0ePHtWvf/1ro2232zV58uQG+1OrAAAAGuf5fcnzO1BjPPv6M98h0CorK/XRRx8Z7ZiYGF166aUhiwcAolGk1BQAQGC05NpsZmam7Ha7nE6npMBcmw3V3DkAQMuFW00BAPiv9c4wjjKffvqpTpw4YbT79+8f8ElpWVlZprY/b4ret2+fqV1SUqLKysoz+tV/Y4bk35to2rdvr7i4//88hoKCAp/3BTxFSk55s3nzZn300Uc6fPiwKisrVVpaqkOHDumTTz7R888/r2HDhmnatGk6efKkz/EgusXGxuqFF15QRkaGpO/eyHTnnXdq5syZ+uKLL1RZWSm3261Tp05pzZo1GjNmjBYtWmTs/1//9V8aOXJkQGJpSR2RzE/hr62t9ZoH1CoEQ7TllTfUKwRSOOVUMFCrEAzRllfeUKsQSK09pw4ePGh6SGBsbKyuuuqqBvtTqxAM0ZZX3lCrEEitMacqKiq0Y8cOzZkzRz/5yU+Un58vSYqLi9Mzzzyj888/v8F9qVUAAACN8/y+5M9CRM/vVqH8vrRw4ULTXItLLrlEbdu2DVk8ABCNIqWmAAACoyXXZm02m2mumuexgh2P1Py5cwCAlgu3mgIA8B9vbm8l3nvvPVM70G+YlqQLLrhAqampKi8vlyR98cUXKioq0llnndXofm63W+vWrTtje2VlpZKTk8/YVl9KSorP8cXExCgxMdGIz9eFvoA3kZJTzVFXV6cVK1Zo06ZNmjdvnvr06dPiYyLyde/eXUuWLNGvf/1rbdq0SS6XS2+88YbeeOONBvfJzMzUPffco5tvvjlgcVRUVJja/uaEZ93xPJ5ErULwRFNeNQf1Cv4Kl5wKBmoVgiWa8qo5qFXwV2vNKZfLpUceecR4WrUkDR8+XF26dGlwH2oVgiWa8qo5qFXwVzjnlMPhUN++fY222+2Wy+U6o9/3v/99/epXv2ry7zu1CgAAoHGe33H8uX/k+d0qVN+X9u3bpz/96U+mbVOmTAlJLAAQzSKhpgAAAqcldUEy14ba2lrV1NQoISGh2fGE69w5AEDTwq2mAAD8x5vbW4GSkhKtXbvWaCclJWnYsGEBP09sbKwuv/xyo+1wOLRw4cIm91u+fLnxNoz6vF1I9NwWHx/vV4z1vyhwoRLNFUk5Vd8555yjcePG6aWXXtKaNWv0xRdfaMeOHVq3bp3mzZun66+/XrGxsUb/Y8eOacKECV7PBXiTk5Ojv/zlL5o1a5bS09Mb7Zubm6v58+cHfFKrZx74+wukZ39qFUItWvKqPuoVrBQOORUM1CoEU7TkVX3UKlipNebUvHnztHnzZqPdtm1bPfDAA43uQ61CMEVLXtVHrYKVwjWn3G636urqjP94W9j+4x//WE8//bRPD3KgVgEAADSuJfeP/L13ZIXKykrdf//9cjgcxrYRI0YoLy8v6LEAQLRr7TUFABBYgZ6r1tLF5MGeOwcACJxwqykAAP+xuL0VWLFihelmy9VXX63U1FRLzjVu3DhTe+HChfrnP//ZYP+dO3fqySef9PpZdXX1GdtqampMbbvd7ld89ScXeTs+4ItIyqnT/vznP2vVqlWaPn26Bg8erE6dOikpKUnx8fHKzs7WlVdeqdmzZ+vNN99Ux44djf2Kior00EMP+TkqRKv9+/dr3Lhx+tWvfqWSkpJG++7atUujRo3ShAkTVFBQELAYAllHvB0v0OegVqEp0ZJXp1GvYLVwyKlgoFYhmKIlr06jVsFqrS2nPvroI82bN89o22w2PfXUU2rbtm2j+1GrEEzRklenUatgtdaWU/WtWLFC1113naZPn66ysrJG+1KrAAAAGteS70ue946C/X3J7Xbr4Ycf1r59+4xtOTk5+vWvfx3UOAAA32nNNQUAEHjBnKvWGuMBAPiOf8MBoPWLC3UAaNrSpUtN7REjRlh2ru9973saPXq03njjDUlSbW2t7rnnHt1444266aabdMEFFyg2Nlb5+flavny5Fi1apKqqKklSSkqK6Uk1ycnJZxzf88k2TqfTr/jqL0hOTEz0a1/gtEjKqdN++MMf+hRPnz59tHDhQo0aNcqY3Ldt2zatW7dOgwcPbu4wEQXWr1+vu+66y/j7abfbddNNN+n666/XBRdcoKSkJBUXF2v79u1688039cknn0iS1q1bpxEjRmjx4sXq3r17i+MIZB3xdrxAn4NahcZEU16dRr2ClcIlp4KBWoVgiaa8Oo1aBSu1tpz617/+pWnTpsntdhvbpkyZoiFDhjS5L7UKwRJNeXUatQpWCuecSkhI0N69e412bW2tSkpK9OWXX2r16tX629/+psrKSrlcLi1dulR79uzRa6+91uDb56lVAACgtRgyZIi+/fZby44/bdo0TZgw4Yzt3r4v+fr2K897R8H+vvTUU09p1apVRrtNmzZ68cUX1aZNm6DGAQDhhpoCAAgHwZyr1hrjAQD4jn/DAaD1483tYW7//v3avn270e7UqZMGDBhg6TlnzJihQYMGGW2Xy6W3335bN910ky666CJdeOGFuu6667RgwQJjgtPIkSN18cUXm46TlpZ2xrE9F+d6fhloSv0n4TS20BdoSKTlVHN069btjBsJH3zwQUCOjciUn5+vKVOmGH8/09LS9Prrr+vxxx9X//79lZaWJrvdrvbt22vo0KF6+eWXNXPmTGP/U6dOafLkycb+LeH5b7+/T0jz7O+tllCrEAzRllfNQb2CP8Ipp4KBWoVgiLa8ag5qFfzR2nJq7969mjRpkuktOKNHj9aUKVN82p9ahWCItrxqDmoV/NHaciouLk7t2rXTwIED9cgjj2j58uXq2bOn8fmePXv0xBNPNLg/tQoAAKBxLbl/ZNW9I1/Mnz9ff/nLX4x2QkKC5s+frx49egQtBgCAWWutKQAAawR6rlpKSkpYxUOtAoDgCbeaAgDwH4vbw9x7771nat9www2y2WyWnjM+Pl5/+tOf9Mtf/lLx8fGN9o2Li9Ndd92lJ554QsePHze2x8TEKDU19Yz+nl8eysvLfY7L5XKZJkXxyx+aI9JyqrlGjhxpGvfGjRsDdmxEnt/97neqrKw02k888YQuuuiiRvcZPXq0Ro8ebbQPHTqkv/71ry2OxfOXxoqKCr/29+zv7ZdQahWCIdryqrmoV/BVOOVUMFCrEAzRllfNRa2Cr1pTTuXn5+uOO+5QaWmpse26667TY4895vMxqFUIhmjLq+aiVsFXrSmnvOncubP+/Oc/m97UvmLFCu3bt89rf2oVAABA4zy/4/hz/8izb7C+L73++uuaM2eO0Y6Li9MLL7ygSy65JCjnBwB41xprCgDAOi2pC5794+LiWvyW3XCeOwcAaFy41RQAgP/iQh0AGuZyufT++++bto0YMSIo546NjdUDDzygn//851q6dKnWr1+vgwcPqri4WElJSerYsaMGDRqkESNGqHv37nK73fr222+N/bt166aYmDOfndChQwdTu7Cw0OeYjh8/rrq6OqOdnZ3djJEhmkViTjVXu3bt1KVLF+Xn50uSjh07JqfTKbvdHrBzIDKUlZXpo48+Mto5OTkaNmyYT/tOmDBBb7zxhtFetmyZ7rjjjhbF05I6IkkFBQXGz6ff7hTIc1Cr4ItozKvmol7BF+GWU8FArYLVojGvmotaBV+0ppwqLCzU7bffbnrg3qBBg/Tcc8/5dV2CWgWrRWNeNRe1Cr5oTTnVmMzMTN16662aP3++se0f//iHzj///DP6UqsAAEBrMWfOHL/fOuWPzp07e93u+X2poKBAHTt29OmYR48eNbWD8X1p2bJl+s1vfmO0bTabnnrqKQ0dOtTycwNAa0FNAQCEg5Zcm3W73ab+nscKdjyStXPnAACNC7eaAgDwH4vbw9j69etNxfLiiy9WTk5OUGPIzs7WpEmTNGnSpEb7HTx40PTWme9973te+3Xv3t3Urr94tymeFyq7devm876AFJk51RLt2rUzJrVKUnFxsdq3bx/w86B127Fjh2miZl5enultX405++yz1blzZx0+fFiStG/fPtXU1LToqWadOnVSYmKiqqurJflXR9xut+lCYk5OjteJ3NQqWC0a86olqFdoSrjlVDBQq2C1aMyrlqBWoSmtJadOnTqlsWPHmupK//799eKLL/r9HY9aBatFY161BLUKTWktOeWLyy67zLS4fe/evV77UasAAEBrYcVcAV94fl86cuSI+vXr59O+9e8deTtWoK1evVozZsyQ2+02tj3yyCMaPny4pecFgNaGmgIACActuTZ74sQJOZ1Oox2Ia7PhPncOANCwcKspAAD/Wf9aEDTb0qVLTe1gvWG6ObZu3Wpq9+nTx2s/zy8Pu3fv9vkcO3fuNLX58gB/RWJOtURVVZWpHcmLTdB8J0+eNLX9nfhcv7/L5VJxcXGL4omJiVHXrl2Ndn5+vioqKnzad//+/aYnUDdUR6hVsFo05lVLUK/QlHDLqWCgVsFq0ZhXLUGtQlNaQ06Vl5dr/Pjx2r9/v7Gtd+/e+tOf/qTExES/j0etgtWiMa9aglqFprSGnPKV51txysvLvfajVgEAADTO8/vSrl27fN43mN+XNm7cqPvuu0+1tbXGtvvuu08///nPLTsnAMA/raWmAACCIysrS23atDHa/lyb9awhgagL4T53DgDQsHCrKQAA/7G4PUyVl5fro48+MtpJSUm69tprQxhR41asWGH8bLfbNWzYMK/90tLSdP755xvt3bt3G086a8q2bdtM7by8vGZEimgVqTnVXC6XS0eOHDGdIy0tLaDnQGTwnOzs67/Zp3lOnk5OTm5xTBdffLHxc11dnbZv3+7Tfr7WEWoVrBaNedVc1Cv4IhxzymrUKlgtGvOquahV8EW451R1dbUmTpxomiR43nnn6ZVXXlFqamqzjkmtgtWiMa+ai1oFX4R7TvnDczF7Q3/fqVUAAACN69evn2Ji/v+0Ms/vQI354osvjJ8zMjJ03nnnBTI0w7///W/deeedcjgcxrbx48dr8uTJlpwPANA8raGmAACCq/5ctZMnTyo/P9+n/TxfHBaoa7PhOncOANC0cKspAAD/sLg9TK1cudI0ieaqq64K+qQ3X+3fv1//+7//a7SHDBmitm3bNth/6NChxs9Op1Nr1qxp8hw1NTVat26d0e7YsaNyc3ObGTGiUSTnVHNs3bpVpaWlRrtnz54BPT4ih+ffvfpvG2uK0+k0/YIYHx9vejpac9WvI5L04Ycf+rSfZz/P4zT0GbUKgRatedUc1Cv4IhxzKhioVbBStOZVc1Cr4Itwzimn06l77rlHW7ZsMbbl5ORo4cKFLb4WQa2ClaI1r5qDWgVfhHNO+cvzLQsdO3ZssC+1CgAAoGHt2rXTRRddZLS/+OILFRYWNrnftm3bVFBQYLQHDx6suLi4gMf35Zdf6pe//KUqKyuNbaNHj9aDDz4Y8HMBAFom3GsKACD4mjtXbdWqVcbPCQkJuuyyy0Iaj9Vz5wAATQu3mgIA8A+L28PUe++9Z2r/7Gc/C1EkTXvqqafkcrkkSTExMbrjjjsa7X/NNdeY2n/961+bPMfy5ctNE/AC/RZrRL5IzqnmWLBggan9wx/+MODnQGTIzc2V3W432ps2bdLx48d92nf16tWmyQR9+/YNSEyXXHKJacLtBx98YKoR3hw6dEgbNmww2r1791aXLl0a7E+tgpWiNa+ag3oFX4RjTgUDtQpWita8ag5qFXwRrjnlcrk0ffp00wK97Oxsvfrqq+rQoUOLj0+tgpWiNa+ag1oFX4RrTvnL7XbrnXfeMW37wQ9+0GB/ahUAAEDj6n/Xqaur01tvvdXkPm+88UaDxwiU/Px8jRs3TiUlJca2G264QY8//njAzwUACIxwrSkAgNAYMmSI6Zr0kiVL5HQ6G91n48aNOnDggNEePHiwkpOTAxJPuM6dAwA0LdxqCgDAPyxuD0P5+fnaunWr0T777LM1YMCAZh3r8OHD6tGjh/GfIUOGBCpMSdKsWbO0fv16oz169GjTUza9yc3N1RVXXGG0N2/erKVLlzbY/9SpU5o9e7bRTkxM1Lhx45odM6JPJOdUcXGxT0+yre8Pf/iDPvvsM6OdkJCg0aNH+x8sokJycrIuvfRSo11TU6Mnn3yyyf2Kior03HPPmbY1lC/+5pXdbtf48eONdllZmZ555pkG+7tcLj3++OPGQyMk6c4772z0HNQqWCka84p6BSuFY04FA7UKVorGvKJWwUrhmlMzZ87UBx98YLTbtWunV199VZ07d25yX19Qq2ClaMwrahWsFI45tX37dh8iN5szZ45pv44dO5rG5YlaBQAA0LhRo0apXbt2RvuVV14xTfz1tGnTJr3//vtGu1evXvrRj37U6Dnmzp1r+p44d+7cRvsXFhZq7NixpocxXX311Zo1a5ZsNltTQwIAhEg41hQAQOhkZmZq1KhRRjs/P18vvfRSg/09r1nbbDZNnjy5wf7hNncOAGCdcKspAAD/sLg9DL377rum9g033BDUGzDz58/Xs88+q6+++qrBPvn5+Zo0aZIWLVpkbOvataumTp3q0znuu+8+xcXFGe1HH31UK1asOKPfN998ozFjxujkyZPGtl/84hfKysry6TyAFNk5dfToUV155ZV67LHHtGXLFrnd7kbPce+992revHmm7b/85S9D9uYotA533XWXqf3hhx/qnnvuafDtTdu3b9ctt9yib7/91tiWmZmpm2++OWAx3XrrrerYsaPR/tvf/qZnn31WDofD1K+8vFzTpk3Txo0bjW39+vXTlVde2eQ5qFWwUrTlFfUKVgvHnAoGahWsFG15Ra2C1cItp2bPnm16M056eroWLlyobt26BeT4p1GrYKVoyytqFawWbjl1xx13aNy4cVq5cqWqqqoa7fvVV19pypQpWrBggWn7ww8/rPj4+Eb3pVYBAAA0LDk52bRAo6amRmPHjtXu3bvP6Lthwwbdeeedpt9Vpk6dGtC5GSUlJRo/frwOHz5sbBs0aJCef/55xcbGBuw8AIDAC7eaAgAIvYkTJyolJcVoz507V6+++qppwbj03UNHx48fb5qDfd111yk3Nzeg8QRjTioAwBrhVlMAAL6zuRubAYWgc7vdGjp0qGky0D/+8Q+dc845zTre4cOHNXToUKPdqVMnrVmzptF9nn32WS1cuFCSlJOToz59+ig7O1vx8fE6efKkdu3apR07dpj26dixo1577TXl5OT4HNsbb7yhmTNnmrb16tVL/fv3V2Jiovbv369PPvlEtbW1xueXXHKJFi5cKLvd7vN5EN0iPad2796t4cOHG+2MjAzl5uaqa9euSktLU0xMjIqKirRz50795z//OeML+rXXXqsXXniBi/9o0gsvvKA//vGPpm3x8fG65JJLdMEFFyg5OVnFxcX64osv9J///MfUz2636+WXX9bAgQO9Hrs5eSVJO3fu1K233mqa3JqVlaXBgwerXbt2Kigo0Jo1a1RaWmp83r59e73zzjvKzs72adzUKlgpmvKKeoVgCMecauiiZ11dnand0KS7p59+2pQ73lCrYKVoyitqFYIhnHKqR48eprbNZlNMjP/PQd21a1eTfahVsFI05RW1CsEQTjnVv39/lZWVGTGcf/756tGjhzIyMpSSkqLKykodP35cu3bt8vpg2bvvvltTpkzxadzUKgAAgMZNnTpVH3zwgdG22Wy67LLL1KNHD9XW1mr79u3atm2baZ9Jkybp/vvvb/LYc+fO1Ysvvmi0p0yZorvvvttr3/fee08PP/ywaVtMTIzfv+cMHz5cTz/9tF/7AAACI1xqymlLly7VjBkzvH5W/95TY9f6fLlODgDw7uOPP9bkyZNN9zS6du2qAQMGKCMjQ4cOHdLatWtVXV1tfH7eeefprbfeUmpqaoPHDbe5cwAA64VbTQnG3FEAiARxTXdBMH3++eemRbgXX3xxsxfhBkJ+fr7y8/Mb7ZOXl6fnn3/e7ze+jB49WmVlZZozZ44xIWj37t1en8YpSQMGDNCcOXOYKAS/RFNOSVJxcbE2bNigDRs2NNovNjZWd955pyZPnsyEVvjk/vvvl91u1/z5841fqhwOhz777DN99tlnDe7Xtm1bPfPMMw1Oam2J3r17a+7cuXrggQdUXFwsSTp27JiWLFnitX/nzp01d+5cvy4iUqtgpWjNK4l6BWuEY055Xoj0t5/ngiRvqFWwUrTmlUStgjXCMadOc7vdPueXv6hVsFK05pVErYI1wjWnHA6Hdu7cqZ07dzbZNy0tTdOnT9eNN97o8/GpVQAAAI2bNWuW6urq9OGHH0r67vedhr4j2mw2/eIXv9B9990X8Di8vb/F1+t9Ld0HABAY4VJTTnO5XD5dw7P6Wh8ARKsrrrhCs2bN0syZM40F5QcPHtTBgwe99u/Vq5defPHFRhchtkSw5s4BAAIv3GpKsOa4AUBr5/9rQ2CppUuXmtojRowIegwDBgzQD3/4QyUnJzfa78ILL9Tvfvc7/eUvf2nWIlxJmjBhgv7617/qBz/4QYNPtuzSpYtmzJihRYsWKSMjo1nnQfSK9JzKzs7W2LFj1bt3b8XFNf28kvT0dN1yyy1asWKFpkyZ0uCTngBvpkyZonfeeUc//elPlZCQ0Gjfdu3aaeLEiVqxYoUGDx5sWUyDBg3S8uXLNXz4cCUlJXntk56erttuu03Lli1r8ClojaFWwUrRklfUKwRLOOZUMFCrYKVoyStqFYIlWnLKE7UKVoqWvKJWIVjCJadmzZqlkSNHKicnx6f+Xbt21f33368PP/zQr4Xtp1GrAAAAGpaQkKA5c+bo2Wef1QUXXNBgv759++qll17SjBkzeLgWAMAragoAwNPw4cP17rvv6sorr2zwgaLt27fXXXfdpbfffludO3e2NJ5gzEkFAFgj3GoKAKBpNre3x9oCkpxOp3bv3q2vv/5aJ0+eVHV1tZKTk3X22WfroosuCvhTxgoLC/Xvf/9bhYWFqq6uVlZWls4991z16dMnoOcBQsXqnHI4HPrqq690+PBhHTt2TBUVFXK5XEpLS1NGRoZ69Oih7t27c8EfAeFwOLRr1y7t379fpaWlqq6uVkpKis466yzl5uaqW7duQf+7VlFRoS1btujo0aMqKSlR27Zt1alTJ/Xv31/x8fEBOQe1ClaKlryiXiFYwjGngoFaBStFS15RqxAs0ZJTnqhVsFK05BW1CsESLjlVVFRk/J0vKipSdXW1EhMTlZqaqg4dOujCCy9Uu3btAnY+ahUAAEDj9u7dq3379qmwsFCxsbHKyspS7969dc4554Q6NABAK0NNAQDUV1RUpK1bt6qgoEAVFRXKzMxUly5d9P3vfz8kD/ENxpxUAIA1wq2mAAC8Y3E7AAAAAAAAAAAAAAAAAAAAAAAAAAAAAMByMaEOAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQ+VjcDgAAAAAAAAAAAAAAAAAAAAAAAAAAAACwHIvbAQAAAAAAAAAAAAAAAAAAAAAAAAAAAACWY3E7AAAAAAAAAAAAAAAAAAAAAAAAAAAAAMByLG4HAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFiOxe0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMuxuB0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAYDkWtwMAAAAAAAAAAAAAAAAAAAAAAAAAAAAALMfidgAAAAAAAAAAAAAAAAAAAAAAAAAAAACA5VjcDgAAAAAAAAAAAAAAAAAAAAAAAAAAAACwHIvbAQAAAAAAAAAAAAAAAAAAAAAAAAAAAACWY3E7AAAAAAAAAAAAAAAAAAAAAAAAAAAAAMByLG4HAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFiOxe0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMuxuB0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAYDkWtwMAAAAAAAAAAAAAAAAAAAAAAAAAAAAALMfidgAAAAAAAAAAAAAAAAAAAAAAAAAAAACA5VjcDgAAAAAAAAAAAAAAAAAAAAAAAAAAAACwHIvbAQAAAAAAAAAAAAAAAAAAAAAAAAAAAACWY3E7AAAAAAAAAAAAAAAAAAAAAAAAAAAAAMByLG4HAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFiOxe0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAMuxuB0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAYDkWtwMAAAAAAAAAAAAAAAAAAAAAAAAAAAAALMfidgAAAAAAAAAAAAAAAAAAAAAAAAAAAACA5VjcDgAAAAAAAAAAAAAAAAAAAAAAAAAAAACw3P8D+x7z8e06cWAAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 2000x700 with 2 Axes>"
       ]
@@ -11215,7 +11510,21 @@
     }
    ],
    "source": [
-    "plot_ate(ate_dist_df)"
+    "plot_ate(ate_dist_df_dr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's worth here expanding on the theory of doubly robust estimation. We showed above the code for implementing the compromise between the treatment assingment estimator and the response or outcome estimator. But why is this useful?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How does Regression Help?"
    ]
   },
   {
diff --git a/examples/causal_inference/bayesian_nonparametric_causal.myst.md b/examples/causal_inference/bayesian_nonparametric_causal.myst.md
index fb1bd55bd..3ea328706 100644
--- a/examples/causal_inference/bayesian_nonparametric_causal.myst.md
+++ b/examples/causal_inference/bayesian_nonparametric_causal.myst.md
@@ -28,6 +28,7 @@ import pandas as pd
 import pymc as pm
 import pymc_bart as pmb
 import pytensor.tensor as pt
+import statsmodels.api as sm
 ```
 
 ```{code-cell} ipython3
@@ -40,9 +41,9 @@ rng = np.random.default_rng(42)
 
 You always run the risk of being wrong. 
 
-Your appetite for that risk is likely proportional to the strength of the claim you are making - less concerned with _casual_ assertions than __causal__ claims! There are few claims stronger than the assertion of a causal relationship. Each such statement is an inferential gamble underwritten by our faith in (occasionally arcance) methodology. 
+Your appetite for that risk is likely proportional to the strength of the claim you are making - less concerned with _casual_ assertions than __causal__ claims!  Each causal statement is an inferential gamble underwritten by our faith in (occasionally arcane) methodology. The conversational stakes are high - you risk your brittle credibility with false claims. There are few claims stronger than the assertion of a causal relationship, few claims more contestable. A naive world model, rich with tenuous connections and non-sequiter implications will expose you as an idiotic charlatan overly impressed by conspiracy theory. We don't that for you. 
 
-In this notebook we will explain and motivate the usage of propensity scores in analysis of causal inference questions. We will avoid the impression of magic - our focus will be on the manner in which we (a) estimate propensity scores and (b) use them in the analysis of causal questions. We will see how they help avoid risks of selection bias in causal inference.
+In this notebook we will explain and motivate the usage of propensity scores in analysis of causal inference questions. We will avoid the impression of magic - our focus will be on the manner in which we (a) estimate propensity scores and (b) use them in the analysis of causal questions. We will see how they help avoid risks of selection bias in causal inference. This method should be comfortable for the Bayeisan analyst who is familiar with weighting and re-weighting their claims with with information in the form priors. Propensity score weighting is just another opportunity to enrich your model with knowledge about the world.
 
 We will illustrate these patterns using two data sets: (i) the NHEFS data used througout Miguel Hernan's _Causal Inference: What If_ book and a second patient focused data set used throughout _Bayesian Nonparametrics for Causal Inference and Missing Data_ by Daniels, Linero and Roy. Throughout we will contrast the use of non-parametric BART models with simpler regression models for the estimation of propensity scores and causal effects.
 
@@ -131,7 +132,7 @@ In this first step we define a model building function to capture the probabilit
 
 We specify two types of model which are to  be assessed. One which relies entirely on the Logistic regression and another which uses BART to model the relationships between and the covariates and the outcome. The BART model has the benefit of using a tree-based algorithm to explore the interaction effects among the various strata in our sample data. 
 
-Having a flexible model like BART is key to understanding what we are doing when we undertake inverse propensity weighting (IVPw) adjustments. The thought is that any given strata in our dataset will be described by a set of covariates. Types of individual will be represented by these covariate profiles. The share of observations within our data which are picked out by any given covariate profile represents a bias towards that type of individual. If our treatment status is such that individuals will more or less actively select themselves into the status, then a naive comparisons of differences between treatment groups and control groups will be misleading to the degree that we have over-represented types of individual (covariate profiles) in the population.
+Having a flexible model like BART is key to understanding what we are doing when we undertake inverse propensity weighting (IVPw) adjustments. The thought is that any given strata in our dataset will be described by a set of covariates. Types of individual will be represented by these covariate profiles - the attribute vector $X$. The share of observations within our data which are picked out by any given covariate profile represents a bias towards that type of individual. If our treatment status is such that individuals will more or less actively select themselves into the status, then a naive comparisons of differences between treatment groups and control groups will be misleading to the degree that we have over-represented types of individual (covariate profiles) in the population.
 
 Randomisation solves this. But we can't always randomise.
 
@@ -191,7 +192,7 @@ These propensity scores can be pulled out and examined alongside the other covar
 
 Firstly, and somewhat superficially, the propensity score is a dimension reduction technique. We take a complex covariate profile $X_{i}$ and reduce it to a scaler $p^{i}_{T}(X)$. It is tool for thinking about the potential outcomes of an individual under different treatment regimes. In a policy evaluation context it can help partial out the degree of incentives for policy adoption strata of the population. 
 
-The pivotal idea is that we cannot license causal claims unless (i) the treatment assignment is independent of the individual's covariate profile $T     \perp\!\!\!\perp X$  and (ii) the outcomes $Y(0)$, and $Y(1)$ and similarly conditionally independent of the treatement $T | X$. If these conditions hold, then we say that $T$ is __strongly ignorable__ under $X$. It is a theorem that if $T$ is strongly ignorable under $X$, then (ii) holds under $p_{T}(X)$. So valid statistical inference proceeds in a lower dimensional space using the propensity score as a proxy for the higher dimensional data. There is a great discussion of the details in Aronow and Miller's _Foundations of Agnostic Statistics_.
+The pivotal idea is that we cannot license causal claims unless (i) the treatment assignment is independent of the covariate profiles i.e $T     \perp\!\!\!\perp X$  and (ii) the outcomes $Y(0)$, and $Y(1)$ and similarly conditionally independent of the treatement $T | X$. If these conditions hold, then we say that $T$ is __strongly ignorable__ given $X$. It is a theorem that if $T$ is strongly ignorable given $X$, then (ii) holds given $p_{T}(X)$. So valid statistical inference proceeds in a lower dimensional space using the propensity score as a proxy for the higher dimensional data. There is a great discussion of the details in Aronow and Miller's _Foundations of Agnostic Statistics_.
 
 We are, as with all causal inference methods, making the assumption of __strong ignorability__. But given this assumption that we are measuring the right covariate profile to induce strong ignorability, then propensity scores can be used thoughtfully to underwrite causal claims. With observational data we cannot re-run the assignment mechanism but we can estimate it, and transform our data to proportionally weight the data summaries within each group so that the analysis is less effected by the over-representation of different strata in each group. This is what we hope to use the propensity scores to achieve. 
 
@@ -241,11 +242,20 @@ These weighting schemes can now be incorporated into various models of statistic
 
 +++
 
+### Robust and Doubly Robust Propensity Scores
+
+We've been keen to stress that IVPw are a corrective. An opportunity for the causal analyst to put their finger on the scale and adjust the representative shares accorded to individuals in the treatment and control groups. As such there are no universal correctives, and naturally a variety of alternatives have arisen to fill gaps where simple propensity score weighting fails. We will see below a number of alternative weighting schemes. 
+
+The main distinction to call out is between the raw propensity score weights and the doubly-robust theory of propensity score weights. 
+
+Doubly robust methods are so named as they represent a compromise estimtator for causal effect that combines (i) a treatment assignment model (like propensity scores) and (ii) a more direct response outcome model. The method combines these two estimators in a way to generate a statistically unbiased estimate of the treatment effect. They work well because the way they are combined requires that only one of the models needs to be well-specified. 
+
++++
+
 ## Estimated Expected Causal Effect (ATE)
 
 The next code block builds a set of functions to pull out an extract a sample from our posterior distribution of propensity scores and use this propensity score to reweight the observed outcome variable across our treatment and control groups to re-calculate the average treatment effect (ATE). It reweights our data using the inverse probability weighting scheme and then plots three views (1) the raw propensity scores across groups (2) the raw outcome distribution and (3) the re-weighted outcome distribution. 
 
-### The Logit Propensity Model
 
 ```{code-cell} ipython3
 def plot_weights(bins, top0, top1, ylim, ax):
@@ -279,7 +289,7 @@ def make_plot(
     ],
     text_pos=(20, 80),
     ps=None,
-    robust=True,
+    method="robust",
 ):
     X = X.copy()
     if ps is None:
@@ -290,20 +300,32 @@ def make_plot(
     else:
         X["ps"] = ps
     X["trt"] = t
-    if robust:
+    propensity0 = X[X["trt"] == 0]["ps"]
+    propensity1 = X[X["trt"] == 1]["ps"]
+    if method == "robust":
         p_of_t = X["trt"].mean()
         X["i_ps"] = np.where(t, (p_of_t / X["ps"]), (1 - p_of_t) / (1 - X["ps"]))
         n_ntrt = X[X["trt"] == 0].shape[0]
         n_trt = X[X["trt"] == 1].shape[0]
-    else:
+    elif method == "raw":
         X["ps"] = np.where(X["trt"], X["ps"], 1 - X["ps"])
         X["i_ps"] = 1 / X["ps"]
         n_ntrt = n_trt = len(X)
-    X["log_y"] = y
+    else:
+        m0 = sm.OLS(y[t == 0], X[t == 0]).fit()
+        m1 = sm.OLS(y[t == 1], X[t == 1]).fit()
+        m0_pred = m0.predict(X)
+        m1_pred = m0.predict(X)
+        X["trt"] = t
+        X["y"] = y
+        p_of_t = X["trt"].mean()
+        X["i_ps"] = np.where(t, (p_of_t / X["ps"]), (1 - p_of_t) / (1 - X["ps"]))
+
+        weighted_outcome0 = (1 - X["trt"]) * (X["y"] - m0_pred) / (1 - X["ps"]) + m0_pred
+        weighted_outcome1 = X["trt"] * (X["y"] - m1_pred) / X["ps"] + m1_pred
+    X["outcome"] = y
 
     bins = np.arange(0.025, 0.85, 0.05)
-    propensity0 = X[X["trt"] == 0]["ps"]
-    propensity1 = X[X["trt"] == 1]["ps"]
     top0, _ = np.histogram(propensity0, bins=bins)
     top1, _ = np.histogram(propensity1, bins=bins)
 
@@ -322,25 +344,43 @@ def make_plot(
     )
 
     bins = lower_bins
+
+    outcome_trt = X[X["trt"] == 1]["outcome"]
+    outcome_ntrt = X[X["trt"] == 0]["outcome"]
     i_propensity0 = X[X["trt"] == 0]["i_ps"]
     i_propensity1 = X[X["trt"] == 1]["i_ps"]
-    outcome_trt = X[X["trt"] == 1]["log_y"]
-    outcome_ntrt = X[X["trt"] == 0]["log_y"]
-    propensity0 = i_propensity0 * outcome_ntrt
-    propensity1 = i_propensity1 * outcome_trt
 
-    top0, _ = np.histogram(propensity0, bins=bins)
-    top1, _ = np.histogram(propensity1, bins=bins)
-    plot_weights(bins, top0, top1, ylims[1], axs[2])
     axs[2].set_ylabel("No. Patients", fontsize=14)
-    axs[2].set_xlabel("Estimated IP Weighted Outcome \n Shifted", fontsize=14)
-    axs[2].text(text_pos[0], text_pos[1], f"Control: E(Y) = {propensity0.sum() / n_ntrt}")
-    axs[2].text(text_pos[0], text_pos[1] - 20, f"Treatment: E(Y) = {propensity1.sum() / n_trt}")
-    axs[2].text(
-        text_pos[0],
-        text_pos[1] - 40,
-        f"tau: E(Y(1) - Y(0)) = {propensity0.sum() / n_ntrt - propensity1.sum() / n_trt}",
-    )
+    if method in ["raw", "robust"]:
+        weighted_outcome1 = outcome_trt * i_propensity1
+        weighted_outcome0 = outcome_ntrt * i_propensity0
+        top0, _ = np.histogram(weighted_outcome0, bins=bins)
+        top1, _ = np.histogram(weighted_outcome1, bins=bins)
+        plot_weights(bins, top0, top1, ylims[1], axs[2])
+        axs[2].set_xlabel("Estimated IP Weighted Outcome \n Shifted", fontsize=14)
+        axs[2].text(text_pos[0], text_pos[1], f"Control: E(Y) = {weighted_outcome0.sum() / n_ntrt}")
+        axs[2].text(
+            text_pos[0], text_pos[1] - 20, f"Treatment: E(Y) = {weighted_outcome1.sum() / n_trt}"
+        )
+        axs[2].text(
+            text_pos[0],
+            text_pos[1] - 40,
+            f"tau: E(Y(1) - Y(0)) = {weighted_outcome0.sum() / n_ntrt - weighted_outcome1.sum() / n_trt}",
+        )
+    else:
+        top0, _ = np.histogram(weighted_outcome0, bins=bins)
+        top1, _ = np.histogram(weighted_outcome1, bins=bins)
+        plot_weights(bins, top0, top1, ylims[1], axs[2])
+        trt = np.mean(X["trt"] * (X["y"] - m1_pred) / X["ps"] + m1_pred)
+        ntrt = np.mean((1 - X["trt"]) * (X["y"] - m0_pred) / (1 - X["ps"]) + m0_pred)
+        axs[2].set_xlabel("Estimated IP Weighted Outcome \n Shifted", fontsize=14)
+        axs[2].text(text_pos[0], text_pos[1], f"Control: E(Y) = {ntrt}")
+        axs[2].text(text_pos[0], text_pos[1] - 20, f"Treatment: E(Y) = {trt}")
+        axs[2].text(
+            text_pos[0],
+            text_pos[1] - 40,
+            f"tau: E(Y(1) - Y(0)) = {ntrt - trt}",
+        )
 
     top0, _ = np.histogram(outcome_ntrt, bins=bins)
     top1, _ = np.histogram(outcome_trt, bins=bins)
@@ -354,48 +394,70 @@ def make_plot(
         text_pos[1] - 40,
         f"tau: E(Y(1) - Y(0)) = {outcome_trt.mean() - outcome_ntrt.mean()}",
     )
+```
 
+## The Logit Propensity Model
 
-make_plot(X, idata_logit)
+We plot the outcome and re-weighted outcome distribution using the robust propensity score estimation method.
+
+```{code-cell} ipython3
+make_plot(X, idata_logit, method="robust")
 ```
 
 Next, and because we are Bayesians - we pull out and evaluate the posterior distribution of the ATE basd on the sampled propensity scores. 
 
 ```{code-cell} ipython3
-def get_ate(X, t, y, i, idata, robust=False):
+def get_ate(X, t, y, i, idata, method="doubly_robust"):
     X = X.copy()
     X["ps"] = idata["posterior"]["p"].stack(z=("chain", "draw"))[:, i].values
-    X["trt"] = t
-    if robust:
+    if method == "robust":
+        X["trt"] = t
         p_of_t = X["trt"].mean()
         X["i_ps"] = np.where(t, (p_of_t / X["ps"]), (1 - p_of_t) / (1 - X["ps"]))
-    else:
+        i_propensity0 = X[X["trt"] == 0]["i_ps"]
+        i_propensity1 = X[X["trt"] == 1]["i_ps"]
+    elif method == "raw":
+        X["trt"] = t
         X["ps"] = np.where(X["trt"], X["ps"], 1 - X["ps"])
         X["i_ps"] = 1 / X["ps"]
+        i_propensity0 = X[X["trt"] == 0]["i_ps"]
+        i_propensity1 = X[X["trt"] == 1]["i_ps"]
+    else:
+        m0 = sm.OLS(y[t == 0], X[t == 0]).fit()
+        m1 = sm.OLS(y[t == 1], X[t == 1]).fit()
+        m0_pred = m0.predict(X)
+        m1_pred = m0.predict(X)
+        X["trt"] = t
+        X["y"] = y
     X["outcome"] = y
-    i_propensity0 = X[X["trt"] == 0]["i_ps"]
-    i_propensity1 = X[X["trt"] == 1]["i_ps"]
     outcome_trt = X[X["trt"] == 1]["outcome"]
     outcome_ntrt = X[X["trt"] == 0]["outcome"]
-    weighted_outcome_ntrt = i_propensity0 * outcome_ntrt
-    weighted_outcome_trt = i_propensity1 * outcome_trt
-    if robust:
+    if method == "robust":
+        weighted_outcome_ntrt = i_propensity0 * outcome_ntrt
+        weighted_outcome_trt = i_propensity1 * outcome_trt
         ntrt = weighted_outcome_ntrt.sum() / len(X[X["trt"] == 0])
         trt = weighted_outcome_trt.sum() / len(X[X["trt"] == 1])
-    else:
+    elif method == "raw":
+        weighted_outcome_ntrt = i_propensity0 * outcome_ntrt
+        weighted_outcome_trt = i_propensity1 * outcome_trt
         ntrt = weighted_outcome_ntrt.sum() / len(X)
         trt = weighted_outcome_trt.sum() / len(X)
+    else:
+        trt = np.mean(X["trt"] * (X["y"] - m1_pred) / X["ps"] + m1_pred)
+        ntrt = np.mean((1 - X["trt"]) * (X["y"] - m0_pred) / (1 - X["ps"]) + m0_pred)
     ate = ntrt - trt
     return [ate, trt, ntrt]
 
 
 qs = range(4000)
-ate_dist = [get_ate(X, t, y, q, idata_logit, robust=True) for q in qs]
+ate_dist = [get_ate(X, t, y, q, idata_logit, method="robust") for q in qs]
 
 ate_dist_df_logit = pd.DataFrame(ate_dist, columns=["ATE", "E(Y(1))", "E(Y(0))"])
 ate_dist_df_logit.head()
 ```
 
+Next we plot the posterior distribution of the ATE. 
+
 ```{code-cell} ipython3
 def plot_ate(ate_dist_df, xy=(-4.5, 250)):
     fig, axs = plt.subplots(1, 2, figsize=(20, 7))
@@ -423,16 +485,16 @@ Note how this estimate of the treatment effect quite different than what we got
 
 +++
 
-### The BART Propensity Model
+## The BART Propensity Model
 
-We apply exactly the same procedures as above, but now we use the propensity distribution achieved using the BART non-parametric model.
+Next we'll apply the doubly robust estimator to the propensity distribution achieved using the BART non-parametric model.
 
 ```{code-cell} ipython3
-make_plot(X, idata_probit)
+make_plot(X, idata_probit, method="doubly_robust", ylims=[(-150, 370), (-220, 150), (-50, 120)])
 ```
 
 ```{code-cell} ipython3
-ate_dist_probit = [get_ate(X, t, y, q, idata_probit, robust=True) for q in qs]
+ate_dist_probit = [get_ate(X, t, y, q, idata_probit, method="doubly_robust") for q in qs]
 ate_dist_df_probit = pd.DataFrame(ate_dist_probit, columns=["ATE", "E(Y(1))", "E(Y(0))"])
 ate_dist_df_probit.head()
 ```
@@ -443,7 +505,9 @@ plot_ate(ate_dist_df_probit)
 
 ### Considerations Choosing between models
 
-It is one thing to evalute change in average over the population, but we might want to allow for the idea of effect heterogenity across the population and as such the BART model is generally better at ensuring accurate predictions and therefore better more calibrated propensity scores acros the deepter strata of our data. 
+It is one thing to evalute change in average over the population, but we might want to allow for the idea of effect heterogenity across the population and as such the BART model is generally better at ensuring accurate predictions and therefore better more calibrated propensity scores acros the deepter strata of our data. But the flexibility of machine learning models for prediction tasks do not guarantee that the propensity scores attributed across the sample are well calibrated to recover the true-treatment effects when used in causal effect estimation. We have to careful in how we use the flexibility of non-parametric models in the causal context. 
+
+First observe the hetereogenous accuracy induced by the BART model across increasingly narrow strata of our sample. 
 
 ```{code-cell} ipython3
 fig, axs = plt.subplots(4, 2, figsize=(20, 25))
@@ -473,12 +537,13 @@ axs[7].set_title("Race/Gender/Active Specific PPC - BART")
 plt.suptitle("Posterior Predictive Checks - Heterogenous Effects", fontsize=20);
 ```
 
-Observations like this go along way to motivating the work in the Double ML causal inference paradigm. The model used to capture the outcome distribution or the propensity score distribution ought to be sensetive to variation across extremities of the data. We can see above that the predictive power of the simpler logistic regression model deterioriates as we progress down the partitions of the data. We will see an example below where it is key to partial out the causal effects across the quantiles of the response distribution. 
+Observations like this go along way to motivating the work in the Double ML causal inference paradigm. The model used to capture the outcome distribution or the propensity score distribution ought to be sensetive to variation across extremities of the data. We can see above that the predictive power of the simpler logistic regression model deterioriates as we progress down the partitions of the data. We will see an example below where this flexibility becomes a problem. 
 
 +++
 
 ### Regression with Propensity Scores
 
+Another perhaps more direct method of causal inference is to just use regression directly. Angrist and Pischke suggest that the familiar properties of regression make it more desirable, but acknowledge that there is a role for propensity and that the methods can be combined by the cautious analyst.
 
 ```{code-cell} ipython3
 def make_prop_reg_model(X, t, y, idata_ps, covariates=None, samples=1000):
@@ -505,6 +570,8 @@ def make_prop_reg_model(X, t, y, idata_ps, covariates=None, samples=1000):
 model_ps_reg, idata_ps_reg = make_prop_reg_model(X, t, y, idata_logit)
 ```
 
+Fitting the regression model using the propensity as a dimensional reduction technique seems to work well here. We recover substantially the same treatment effect estimate as above. 
+
 ```{code-cell} ipython3
 az.summary(idata_ps_reg)
 ```
@@ -519,6 +586,8 @@ az.summary(idata_ps_reg_bart)
 
 ### Causal Inference as Regression Imputation
 
+Above we read-off the causal effect estimate as the coefficient on the treatment variable in our regression model. An arguably more direct approach uses the fitted regression models to impute the distribution of potential outcomes under different treatment regimes. In this way we have yet another perspective on causal inference 
+
 ```{code-cell} ipython3
 X_mod = X.copy()
 X_mod["ps"] = ps = idata_probit["posterior"]["p"].mean(dim=("chain", "draw")).values
@@ -557,8 +626,14 @@ idata_ntrt["posterior_predictive"]["pred"].mean()
 idata_trt["posterior_predictive"]["pred"].mean() - idata_ntrt["posterior_predictive"]["pred"].mean()
 ```
 
+All perspectives on the question of causal inference here seem broadly convergent. Next we'll see an example where the choices an analyst makes can go quite wrong. 
+
++++
+
 ## Health Expenditure Data
 
+We will begin with looking a health-expenditure data set analysed in _Bayesian Nonparametrics for Causal Inference and Missing Data_ . The telling feature about this data set is absence of obvious causal impact on expenditure due to the presence of smoking. We follow the authors and try and model the effect of `smoke` on the logged out `log_y`.
+
 ```{code-cell} ipython3
 df = pd.read_csv("../data/meps_bayes_np_health.csv", index_col=["Unnamed: 0"])
 df = df[df["totexp"] > 0].reset_index(drop=True)
@@ -568,6 +643,8 @@ df["smoke"] = np.where(df["smoke"] == "No", 0, 1)
 df
 ```
 
+### Some basic Summaries
+
 ```{code-cell} ipython3
 raw_diff = df.groupby("smoke")[["log_y"]].mean()
 print("Treatment Diff:", raw_diff["log_y"].iloc[0] - raw_diff["log_y"].iloc[1])
@@ -599,6 +676,8 @@ print(
 strata_expected_df
 ```
 
+It certaintly seems that there is little to no impact due to our treatment effect in the data. Can we recover this insight using the method of inverse propensity score weighting. But first, let's do some basic exploratory data analysis to confirm our intuition. 
+
 ```{code-cell} ipython3
 fig, axs = plt.subplots(2, 2, figsize=(20, 8))
 axs = axs.flatten()
@@ -651,6 +730,8 @@ axs[1].legend()
 axs[1].set_title("Empirical Cumulative \n Densities");
 ```
 
+The plots would seem to confirm undifferentiated nature of the outcome across the two groups. With some hint of difference at the outer quantiles  of the distribution. 
+
 ```{code-cell} ipython3
 qs = np.linspace(0.05, 0.99, 100)
 quantile_diff = (
@@ -672,6 +753,8 @@ axs[1].plot(quantile_diff["quantile"], quantile_diff["diff"])
 axs[1].set_title("Differences across the Quantiles");
 ```
 
+### What could go Wrong?
+
 ```{code-cell} ipython3
 dummies = pd.concat(
     [
@@ -702,31 +785,60 @@ X
 ```
 
 ```{code-cell} ipython3
-m_ps_expend, idata_expend = make_propensity_model(X, t, bart=True, probit=True, samples=1000)
+m_ps_expend_bart, idata_expend_bart = make_propensity_model(
+    X, t, bart=True, probit=True, samples=4000
+)
+m_ps_expend_logit, idata_expend_logit = make_propensity_model(X, t, bart=False, samples=1000)
 ```
 
+### Non-Parametric BART Model Propensity Model is mis-specified
+
+The flexibility of the BART model fit is poorly calibrated to recover the average treatment effect. Let's evaluate the weighted outcome distributions under the robust IVPw estimate. 
+
 ```{code-cell} ipython3
+ps = idata_expend_bart["posterior"]["p"].mean(dim=("chain", "draw")).values
 make_plot(
     X,
-    idata_expend,
-    ylims=[(-100, 260), (-40, 270), (-130, 400)],
-    lower_bins=np.arange(6, 45, 1),
+    idata_expend_bart,
+    ylims=[(-100, 340), (-70, 380), (-130, 420)],
+    lower_bins=np.arange(6, 15, 1),
     text_pos=(11, 200),
-    robust=False,
+    method="robust",
+    ps=ps,
 )
 ```
 
+This is a __disastrous__ result. Evaluated at the expected values of the posterior propensity score distribution the robust IPW estimator of ATE suggests a substantial difference in the treatment and control groups. What is going on?
+
+What happens if we look at the posterior ATE distributions under different estimators?
+
 ```{code-cell} ipython3
 qs = range(4000)
-ate_dist = [get_ate(X, t, y, q, idata_expend, robust=True) for q in qs]
-ate_dist_df = pd.DataFrame(ate_dist, columns=["ATE", "E(Y(1))", "E(Y(0))"])
-ate_dist_df.head()
+ate_dist = [get_ate(X, t, y, q, idata_expend_bart, method="doubly_robust") for q in qs]
+ate_dist_df_dr = pd.DataFrame(ate_dist, columns=["ATE", "E(Y(1))", "E(Y(0))"])
+
+ate_dist = [get_ate(X, t, y, q, idata_expend_bart, method="robust") for q in qs]
+ate_dist_df_r = pd.DataFrame(ate_dist, columns=["ATE", "E(Y(1))", "E(Y(0))"])
+
+ate_dist_df_dr.head()
 ```
 
 ```{code-cell} ipython3
-plot_ate(ate_dist_df)
+plot_ate(ate_dist_df_r)
 ```
 
+Deriving ATE estimates across draws from the posterior distribution and averaging these seems to give a more sensible figure, but still inflated. If instead we use the doubly robust estimator we recover a much more sensible figure. 
+
+```{code-cell} ipython3
+plot_ate(ate_dist_df_dr)
+```
+
+It's worth here expanding on the theory of doubly robust estimation. We showed above the code for implementing the compromise between the treatment assingment estimator and the response or outcome estimator. But why is this useful?
+
++++
+
+### How does Regression Help?
+
 ```{code-cell} ipython3
 model_ps_reg_expend, idata_ps_reg_expend = make_prop_reg_model(X, t, y, idata_expend)
 ```