Heterogeneous effect estimation is crucial in causal inference, with applications across medicine and social science. Many methods for estimating conditional average treatment effects (CATEs) have been proposed, but there are gaps in understanding if and when such methods are optimal. This is especially true when the CATE has nontrivial structure (e.g., smoothness or sparsity). Our work contributes in several ways. First, we study a two-stage doubly robust CATE estimator and give a generic error bound, which yields rates faster than much of the literature. We apply the bound to derive error rates in smooth nonparametric models, and give sufficient conditions for oracle efficiency. Along the way we give a general error bound for regression with estimated outcomes; this is the second main contribution. The third contribution is aimed at understanding the fundamental statistical limits of CATE estimation. To that end, we propose and study a local polynomial adaptation of double-residual regression. We show that this estimator can be oracle efficient under even weaker conditions, and we conjecture that they are minimal in a minimax sense. We go on to give error bounds in the non-trivial regime where oracle rates cannot be achieved. Some finite-sample properties are explored with simulations.
This research was supported by NSF DMS Grant 1810979, NSF CAREER Award 2047444, and NIH R01 Grant LM013361-01A1.
The author thanks Sivaraman Balakrishnan, Matteo Bonvini, Aaron Fisher, Virginia Fisher, Jamie Robins, and Larry Wasserman for very helpful discussions.
"Towards optimal doubly robust estimation of heterogeneous causal effects." Electron. J. Statist. 17 (2) 3008 - 3049, 2023. https://doi.org/10.1214/23-EJS2157