Introduction to Machine Learning for Causal Analysis Using Observational Data (18 October 2022)

Programme

9:00-9:30 Registration and Welcome
9:30-10:30 A Quick Introduction to Machine Learning
- Supervised ML
- Regression classification
- Random forests
10:30-10:50 Drinks Break
10:50-12:30 Python practical 1
- Using Python within Google Colab to train, test and assess
12:30-13:30 Lunch Break
13:30-14:30 Causal inference and ML
- Potential outcomes and average treatment effects
- No unobserved confounding: handling covariate differences
- Regression and propensity scores
14:30-16:30 Python practical 2
- Using Python to estimate causal effects using Google Colab
15:00 Drinks available
16:30-17:00 Consolidation and Discussion

Create a Google account if you do not have one already.
Go to https://colab.research.google.com/.
If you see a “Sign in” button in the top right corner of the screen, click it and sign in using your Google account. If you see your account’s profile picture instead, you are already signed in.
In the top right corner of the screen, there is also a “Connect” button. Click it. A successful connection will confirm you are logged in correctly.
Feel free to explore the default “Welcome to Colaboratory” notebook (the one opened by default when you visit the website). Execute some code cells and familiarise yourself with the environment. This step is entirely optional as we will cover this in the course.

Introduction to Causal Inference online course.
Recent causal inference surveys:
- https://doi.org/10.1145/3397269
- http://arxiv.org/abs/2002.02770
EconML.
Causal decision making link.
Python courses.
Scikit-learn docs.
Scientific Python ecosystem - SciPy.

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