diff --git a/src/utils.jl b/src/utils.jl
index 071d8e2..740e006 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -152,14 +152,34 @@ function make_float!(dataset, colnames)
     end
 end
 
-function coerce_types!(dataset, colnames)
-    infered_types = autotype(dataset[!, colnames])
+function coerce_types!(dataset, colnames; rules=:few_to_finite)
+    infered_types = autotype(dataset[!, colnames], rules)
     coerce!(dataset, infered_types)
 end
 
-coerce_types!(dataset, Ψ::TMLE.Estimand) =
-    coerce_types!(dataset, collect(variables(Ψ)))
+"""
+Outcomes and Treatment variables must be dealt with differently until there are models dealing specificaly with Count data.
+
+- Outcomes need to be binary, i.e. OrderedFactor{2} or Continuous
+- Treatments need to be Categorical
+- Other variables can be dealt with either way
+"""
+function coerce_types!(dataset, Ψ::TMLE.Estimand)
+    all_outcomes = outcomes(Ψ)
+    for outcome in all_outcomes
+        if isbinary(outcome, dataset)
+            coerce_types!(dataset, [outcome], rules=:few_to_finite)
+        else
+            coerce_types!(dataset, [outcome], rules=:discrete_to_continuous)
+        end
+    end
+    other_variables = collect(setdiff(variables(Ψ), all_outcomes))
+    coerce_types!(dataset, other_variables, rules=:few_to_finite)
+end
+
+outcomes(Ψ::TMLE.Estimand) = Set([Ψ.outcome])
 
+outcomes(Ψ::TMLE.ComposedEstimand) = union((outcomes(arg) for arg in Ψ.args)...)
 
 variables(Ψ::TMLE.ComposedEstimand) = union((variables(arg) for arg in Ψ.args)...)
 
diff --git a/test/sieve_variance.jl b/test/sieve_variance.jl
index 5bf29a2..ef47781 100644
--- a/test/sieve_variance.jl
+++ b/test/sieve_variance.jl
@@ -44,6 +44,7 @@ function write_sieve_dataset(sample_ids)
 
     dataset[!, "CONTINUOUS, OUTCOME"] = y₁
     dataset[!, "BINARY/OUTCOME"] = categorical(y₂)
+    dataset[!, "COUNT_OUTCOME"] = rand(rng, [1, 2, 3, 4], n)
 
     CSV.write("data.csv", dataset)
 end
diff --git a/test/testutils.jl b/test/testutils.jl
index db9f450..969d7cd 100644
--- a/test/testutils.jl
+++ b/test/testutils.jl
@@ -51,7 +51,7 @@ function statistical_estimands_only_config()
                 outcome_extra_covariates = (:C1,)
             ),
             CM(
-                outcome = Symbol("CONTINUOUS, OUTCOME"), 
+                outcome = Symbol("COUNT_OUTCOME"), 
                 treatment_values = (
                     T1 = true, 
                     T2 = false), 
@@ -124,7 +124,7 @@ function build_dataset(;n=1000, format="csv")
     dataset[!, "CONTINUOUS, OUTCOME"] = y₁
     # Slash in name
     dataset[!, "BINARY/OUTCOME"] = y₂
-    dataset[!, "EXTREME_BINARY"] = vcat(0, ones(n-1))
+    dataset[!, "COUNT_OUTCOME"] = rand(rng, [1, 2, 3, 4], n)
 
     return dataset
 end
diff --git a/test/utils.jl b/test/utils.jl
index 9a31afb..517154e 100644
--- a/test/utils.jl
+++ b/test/utils.jl
@@ -68,39 +68,48 @@ end
     )
 end
 @testset "Test coerce_types!" begin
-    Ψ = IATE(
-        outcome=:Ycont,
-        treatment_values=(T₁=(case=1, control=0), T₂=(case="AC", control="CC")),
-        treatment_confounders=(T₁=[:W₁, :W₂], T₂=[:W₁, :W₂]),
-    )
-
     dataset = DataFrame(
         Ycont  = [1.1, 2.2, missing, 3.5, 6.6, 0., 4.],
-        Ycat = [1., 0., missing, 1., 0, 0, 0],
+        Ybin = [1., 0., missing, 1., 0, 0, 0],
+        Ycount = [1, 0., missing, 1, 2, 0, 3],
         T₁ = [1, 0, missing, 0, 0, 0, missing],
         T₂ = [missing, "AC", "CC", "CC", missing, "AA", "AA"],
         W₁ = [1., 0., 0., 1., 0., 1, 1],
         W₂ = [missing, 0., 0., 0., 0., 0., 0.],
         C = [1, 2, 3, 4, 5, 6, 6]
     )
+    # Continuous Outcome
+    Ψ = IATE(
+        outcome=:Ycont,
+        treatment_values=(T₁=(case=1, control=0), T₂=(case="AC", control="CC")),
+        treatment_confounders=(T₁=[:W₁, :W₂], T₂=[:W₁, :W₂]),
+    )
     TargetedEstimation.coerce_types!(dataset, Ψ)
-
     @test scitype(dataset.T₁) == AbstractVector{Union{Missing, OrderedFactor{2}}}
     @test scitype(dataset.T₂) == AbstractVector{Union{Missing, Multiclass{3}}}
     @test scitype(dataset.Ycont) == AbstractVector{Union{Missing, MLJBase.Continuous}}
     @test scitype(dataset.W₁) == AbstractVector{OrderedFactor{2}}
     @test scitype(dataset.W₂) == AbstractVector{Union{Missing, OrderedFactor{1}}}
-
+    
+    # Binary Outcome
     Ψ = IATE(
-        outcome=:Ycat,
+        outcome=:Ybin,
         treatment_values=(T₂=(case="AC", control="CC"), ),
         treatment_confounders=(T₂=[:W₂],),
         outcome_extra_covariates=[:C]
     )
     TargetedEstimation.coerce_types!(dataset, Ψ)
-
-    @test scitype(dataset.Ycat) == AbstractVector{Union{Missing, OrderedFactor{2}}}
+    @test scitype(dataset.Ybin) == AbstractVector{Union{Missing, OrderedFactor{2}}}
     @test scitype(dataset.C) == AbstractVector{Count}
+
+    # Count Outcome
+    Ψ = IATE(
+        outcome=:Ycount,
+        treatment_values=(T₂=(case="AC", control="CC"), ),
+        treatment_confounders=(T₂=[:W₂],),
+    )
+    TargetedEstimation.coerce_types!(dataset, Ψ)
+    @test scitype(dataset.Ycount) == AbstractVector{Union{Missing, MLJBase.Continuous}}
 end
 
 @testset "Test misc" begin