T-Learner – together we can do more
In this section, we’ll learn what T-Learner is and how it’s different from S-Learner. We’ll implement the model using DoWhy and EconML and compare its performance with the model from the previous section. Finally, we’ll discuss some of the drawbacks of T-Learner before concluding the section.
Forcing the split on treatment
The basic motivation behind T-Learner is to overcome the main limitation of S-Learner. If S-Learner can learn to ignore the treatment, why not make it impossible to ignore the treatment?
This is precisely what T-Learner is. Instead of fitting one model on all observations (treated and untreated), we now fit two models – one only on the treated units, and the other one only on the untreated units.
In a sense, this is equivalent to forcing the first split in a tree-based model to be a split on the treatment variable. Figure 9.12 presents a visual presentation of this concept:
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