Machine Learning Methods for Estimating Heterogeneous Causal Eects

In this paper we study the problems of estimating heterogeneity in causal eects in experimental or observational studies and conducting inference about the magnitude of the dierences in treatment eects across subsets of the population. In applications, our method provides a data-driven approach to determine which subpopulations have large or small treatment eects and to test hypotheses about the dierences in these eects. For experiments, our method allows researchers to identify heterogeneity in treatment eects that was not specied in a pre-analysis plan, without concern about invalidating inference due to multiple testing. In most of the literature on supervised machine learning (e.g. regression trees, random forests, LASSO, etc.), the goal is to build a model of the relationship between a unit’s attributes and an observed outcome. A prominent role in these methods is played by cross-validation which compares predictions to actual outcomes in test samples, in order to select the level of complexity of the model that provides the best predictive power. Our method is closely related, but it diers in that it is tailored for predicting causal eects of a treatment rather than a unit’s outcome. The challenge is that the \ground truth" for a causal eect is not observed for any individual unit: we observe the unit with the treatment,