Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

ArXi:2603.16829v1 Announce Type: cross 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.