Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation

arXiv:2402.14264v1 Announce Type: cross
Abstract: Average treatment effect estimation is the most central problem in causal inference with application to numerous disciplines. While many estimation strategies have been proposed in the literature, recently also incorporating generic machine learning estimators, the statistical optimality of these methods has still remained an open area of investigation. In this paper, we adopt the recently introduced structure-agnostic framework of statistical lower bounds, which poses no structural properties on the nuisance functions other than access to …

abstract application arxiv causal inference cs.lg econ.em inference literature machine machine learning math.st robust statistical stat.me stat.ml stat.th strategies treatment type