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 ...

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