Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
Abstract
Beyond conditional average treatment effects, treatments may impact the entire outcome distribution in covariate-dependent ways, for example, by altering the variance or tail risks for specific subpopulations. We propose a novel estimand to capture such conditional distributional treatment effects, and develop a doubly robust estimator that is minimax optimal in the local asymptotic sense. Using this, we develop a test for the global homogeneity of conditional potential outcome distributions that accommodates discrepancies beyond the maximum mean discrepancy (MMD), has provably valid type 1 error, and is consistent against fixed alternatives -- the first test, to our knowledge, with such guarantees in this setting. Furthermore, we derive exact closed-form expressions for two natural discrepancies (including the MMD), and provide a computationally efficient, permutation-free algorithm for our test.
Cite
@article{arxiv.2603.16829,
title = {Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing},
author = {Saksham Jain and Alex Luedtke},
journal= {arXiv preprint arXiv:2603.16829},
year = {2026}
}