Wrapping it up

Congratulations! You just reached the end of Chapter 10.

In this chapter, we introduced four new causal estimators: DR-Learner, TMLE, DML, and Causal Forest. We used two of them on our synthetic earnings dataset, comparing their performance to the meta-learners from Chapter 9.

After that, we learned about the differences in workflows between observational and experimental data and fit six different models to the Hillstrom dataset. We discussed popular metrics used to evaluate uplift models and learned how to use confidence intervals for EconML estimators. We discussed when using machine learning models for heterogeneous treatment effects can be beneficial from an experimental point of view. Finally, we summarized the differences between different models and closed the chapter with a short discussion on counterfactual model explanations.

In the next chapter, we’ll continue our journey through the land of causal inference with machine learning, and with...