Causal Inference and Machine Learning – Advanced Estimators, Experiments, Evaluations, and More
Welcome to Chapter 10!
We closed the previous chapter by discussing meta-learners. We started with a single model S-Learner and finished with a complex X-Learner that required us to train five machine learning models behind the scenes!
Each new model was an attempt to overcome the limitations of its predecessors. In this chapter, we’ll continue to walk the path of improvement. Moreover, we’ll integrate some of the approaches introduced in the previous chapter in order to make our estimates better and decrease their variance.
In this chapter, we’ll learn about doubly robust (DR) methods, double machine learning (DML), and Causal Forests. By the end of this chapter, you’ll have learned how these methods work and how to implement them using EconML by applying them to real-world experimental data. You’ll also have learned about the concept of...
