Open AllardJM opened 3 years ago
@AllardJM , I am also confused how I can interpret the heterogeneous treatment effect point estimate values.
In your example, Treatment is categorical. 'No E-Mail' - 1 'Mens E-Mail' - 2 'WoMens E-Mail' - 3
In your inference, infer_result = causal_forest.effect_inference(X =X,T0 =1 , T1 =3 )
From this discussion issue-676 The treatment effect is the estimated average effect on Y from moving from T=1 to T=3, given X.
Let's consider, first test sample - X.iloc[[0]], the point estimate is 0.074
If we want to describe the effect on this first test sample, if the treatment is changed from T=1 ('No E-Mail') to T=3 ('WoMens E-Mail') then the Y "customer visit" will be increased by 0.074.
Is it correct understanding ?
Hello!
I didn't see any examples where there existed a discrete treatment with multiple values (>2) and a binary outcome. I am hopeful someone can confirm my understanding.
This data set is from a marketing campaign where customers received one of three treatments (https://blog.minethatdata.com/2008/03/minethatdata-e-mail-analytics-and-data.html):
The outcome I chose here is if the customer visited after the campaign, or not.
Lets say the research question was if the treatment effect depended on the customers prior purchase categories (of which Mens and Womens are binary values in the data)
Here I am setting the treatment to a numeric (1,2,3) for the three categories and using a regression wrapper function to overcome the fact that econml doesnt natively support non-numeric outcomes.
The inference for the treatment effect of Womans email versus no email is here (Mens would be simiiar)
and the result:
Is this the proper way to conduct this analysis using Casual Forest?