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The holy grail of causal inference is the individual-level treatment effect: how would a particular patient respond to a drug? Which users will respond most to a targeted ad? Would a given student be helped or harmed by a classroom intervention? This session introduces machine learning tools for estimating heterogeneous treatment effects like random causal forests. The course goes over the theory and concepts as well as the nitty-gritty of coding the methods up in python, R, and Stata using real-world examples. This course can be taken as a follow-up to the Machine Learning and Causal Inference mixtape session, or as a stand-alone course.

1. Review of Machine Learning and Causal Infernece Course
   • Potential outcomes and treatment effects
   • Basic causal inference summary
   • Prediction Target
   • Prediction Methods
   • Prediction mechanics
   • Decision Trees
   • Forest for the Trees
2. Combining causal effects and ML: predicting heterogeneous treatment effects
   • Traditional heterogeneity analysis: Interacted regression
   • Challenges with traditional heterogeneity analysis
   • Predicting outcomes vs. treatment effects
   • Adapting ML to predict treatment effects

The following is a set of readings for analyzing heterogeneous effects with machine learning methods:

Athey and Imbens (2016): Introduction to using trees to estimate heterogeneous treatment effects.

Athey, Tibshirani, and Wager (2019): Warning! Very technical material. But contains the theory for using machine learning to estimate heterogeneous effects in a wide class of settings

Athey and Wager (2019): A more accessible application of the methods we will be using

Wager and Athey (2018): Technical/theoretical background for random causal forests

Heterogeneous Effects

1. ML + Heterogeneous Effects