B0592
Title: Optimal estimation of heterogeneous causal effects
Authors: Edward Kennedy - Carnegie Mellon University (United States) [presenting]
Abstract: Estimation of heterogeneous causal effects - i.e., how effects of policies and treatments vary across units - is fundamental to medical, social, and other sciences, and plays a crucial role in optimal treatment allocation, generalizability, subgroup effects, and more. Many methods for estimating conditional average treatment effects (CATEs) have been proposed in recent years. Still, there have remained important theoretical gaps in understanding if and when such methods make optimally efficient use of the data at hand. This is especially true when the CATE has a nontrivial structure (e.g., smoothness or sparsity). Work across two recent papers in this context is surveyed. First, we study a two-stage doubly robust estimator and give a generic model-free error bound, which, despite its generality, yields sharper results than those in the current literature. The second contribution is aimed at understanding the fundamental statistical limits of CATE estimation. We resolve this long-standing problem by deriving a minimax lower bound, with a matching upper bound obtained via a new estimator based on higher-order influence functions. Applications in medicine and political science are considered.
