Causal Inference and Machine Learning – from Matching to Meta-Learners
Welcome to Chapter 9!
In this chapter, we’ll see a number of methods that can be used to estimate causal effects in non-linear cases. We’ll start with relatively simple methods and then move on to more complex machine learning estimators.
By the end of this chapter, you’ll have a good understanding of what methods can be used to estimate non-linear (and possibly heterogeneous (or individualized)) causal effects. We’ll learn about the differences between four different ways to quantify causal effects: average treatment effect (ATE), average treatment effect on the treated (ATT), average treatment effect on the control (ATC), and conditional average treatment effect (CATE).
In this chapter, we will cover the following key topics:
- ATE, ATT, ATC, and CATE
- Matching
- Propensity scores
- Inverse probability weighting
- Meta-learners
