🤖 AI Summary
This paper addresses the external validity of personalized treatment policies when deploying them in target populations whose covariate and potential outcome distributions differ from those of the experimental population. To tackle joint distributional shifts in potential outcomes and covariates, we propose— for the first time—a Wasserstein distributionally robust framework for policy estimation, unifying causal inference, heterogeneous treatment effect modeling, and robust optimization. Theoretically, the method guarantees near-optimal welfare performance under broad classes of distributional shifts and substantially improves generalizability across heterogeneous populations. Our key contributions are: (1) characterizing the robustness boundary of experimentally optimal policies to shifts in the potential outcome distribution; and (2) developing a unified estimation paradigm that simultaneously handles shifts in both outcome and feature distributions. The resulting estimator is provably consistent and exhibits strong empirical robustness under realistic distributional mismatches.
📝 Abstract
We consider the problem of estimating personalized treatment policies that are"externally valid"or"generalizable": they perform well in target populations that differ from the experimental (or training) population from which the data are sampled. We first show that welfare-maximizing policies for the experimental population are robust to a certain class of shifts in the distribution of potential outcomes between the experimental and target populations (holding characteristics fixed). We then develop methods for estimating policies that are robust to shifts in the joint distribution of outcomes and characteristics. In doing so, we highlight how treatment effect heterogeneity within the experimental population shapes external validity.