S-Learner – the Lone Ranger
With this section, we begin our journey into the world of meta-learners. We’ll learn why ATE is sometimes not enough and we’ll introduce heterogeneous treatment effects (HTEs) (also known as conditional average treatment effects or individualized treatment effects). We’ll discuss what meta-learners are, and – finally – we’ll implement one (S-Learner) to estimate causal effects on a simulated dataset with interactions (we’ll also use it on real-life experimental data in Chapter 10).
By the end of this section, you will have a solid understanding of what CATE is, understand the main ideas behind meta-learners, and learn how to implement S-Learner using DoWhy and EconML on your own.
Ready?
The devil’s in the detail
In the previous sections, we computed two different types of causal effects: ATE and ATT. Both ATE and ATT provide us with information about the estimated average causal effect...
