Professor David Blackwell (1919 – 2010) was a distinguished American statistician and mathematician who made significant contributions to game theory, probability theory, information theory, and Bayesian statistics. He is one of the eponyms of the Rao–Blackwell theorem and became the first African-American elected member of the US National Academy of Sciences. As a Department, we wanted to mark his ground-breaking work and are delighted to hold this annual lecture named after him in October each year.

Watch the talk here.

Title: "Introducing the Forster-Warmuth Nonparametric Counterfactual Regression"

Abstract: Series regression estimation is one of the most popular non-parametric regression techniques in practice. The most routinely used series estimator is based on ordinary least squares fitting, which is known to be minimax rate optimal in various settings, albeit under stringent restrictions on the basis functions. In this work, inspired by the recently developed Forster-Warmuth (FW) regression, we propose an alternative nonparametric series estimator that can attain minimax estimation rates under strictly weaker conditions imposed on the basis functions, than virtually all existing series estimators in the literature. Another contribution of this work generalizes the FW-regression to a so-called counterfactual regression problem, in which the response variable of interest may not be directly observed (hence, the name ``counterfactual'') on all sampled units. Although counterfactual regression is not entirely a new area of inquiry, we propose the first-ever systematic study of this challenging problem from a unified pseudo-outcome perspective. In fact, we provide what appears to be the first generic and constructive approach for generating the pseudo-outcome (to substitute for the unobserved response) which leads to the estimation of the counterfactual regression curve of interest with small bias, namely bias of second order. Several applications are used to illustrate the resulting FW counterfactual regression including a large class of nonparametric regression problems in missing data and causal inference literature, for which we establish conditions for minimax rate optimality. This is joint work with Yachong Yang and Arun kuchibhotla.

Biography: Eric J Tchetgen Tchetgen is The Luddy Family President's Distinguished Professor and Professor of Biostatistics at the Perelman School of Medicine, and Professor Statistics and Data Science at the Wharton School of the University of Pennsylvania. He also co-directs the Penn Center for Causal Inference, which supports the development and dissemination of causal inference methods in Health and Social Sciences.  He has published extensively on Causal Inference, Missing Data and Semiparametric Theory with several impactful applications ranging from HIV research, Genetic Epidemiology, Environmental Health and Alzheimer's Disease and related aging disorders. He is an Amazon scholar working with Amazon scientists on a variety of causal inference problems in the Tech industry space.  Professor Tchetgen Tchetgen is an 2022 inaugural co-recipient of the newly established Rousseeuw Prize for statistics in recognition for his work in Causal Inference with applications in Public Health and Medicine.

A list of David Blackwell Lectures can be found here.