Hawes Hall, Classroom 203, Harvard Business School
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Optimal nonparametric estimation of heterogeneous causal effects
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, but 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 nontrivial structure (e.g., smoothness or sparsity). This talk surveys work across two recent papers in this context. First, we study a two-stage doubly robust estimator and give a new 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 matching upper bound obtained via a new estimator based on higher order influence functions. Applications in medicine and political science are considered.
Edward Kennedy is an associate professor of Statistics & Data Science at Carnegie Mellon University. He joined the department after graduating with a PhD in biostatistics from the University of Pennsylvania. Edward’s methodological interests lie at the intersection of causal inference, machine learning, and nonparametric theory, especially in settings involving high-dimensional and otherwise complex data. His applied research focuses on problems in criminal justice, health services, medicine, and public policy. Edward is a recipient of the NSF CAREER award, the David P. Byar Young Investigator award, and the Thomas Ten Have Award for exceptional research in causal inference.
This event is part of the Causal Seminar Series.