``Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments.''

 

  Abstract

Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with many covariates and small sample size. In addition, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for heterogeneous treatment effects discovered by a generic ML algorithm. We apply the Neyman's repeated sampling framework to a common setting, in which researchers use an ML algorithm to estimate the conditional average treatment effect and then divide the sample into several groups based on the magnitude of the estimated effects. We show how to estimate the average treatment effect within each of these groups, and construct a valid confidence interval. In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects. The validity of our methodology does not rely on the properties of ML algorithms because it is solely based on the randomization of treatment assignment and random sampling of units. Finally, we generalize our methodology to the cross-fitting procedure by accounting for the additional uncertainty induced by the random splitting of data. The open-source software package is available for implementing the proposed methodology.

  Software and Related Paper

Imai, Kosuke and Michael Lingzhi Li. (2023). ``Experimental Evaluation of Individualized Treatment Rules.'' Journal of the American Statistical Association, Vol. 118, No. 541, pp. 242-256.
Li, Michael Lingzhi and Kosuke Imai. ``Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning.''
Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li. ``The Cram Method for Efficient Simultaneous Learning and Evaluation.''
Li, Michael Lingzhi and Kosuke Imai. ``evalITR: Evaluating Individualized Treatment Rules.'' available through The Comprehensive R Archive Network and GitHub

© Kosuke Imai
 Last modified: Thu Mar 14 21:36:14 EDT 2024