Event Date
Speaker: Christopher Harshaw, FODSI Post-Doctoral Fellow, Massachusetts Institute of Technology / UC Berkeley
Title: "Algorithm Design for Randomized Experiments"
Abstract: Randomized experiments are one of the most reliable causal inference methods and are used in a variety of disciplines from clinical medicine, public policy, economics, and corporate A/B testing. Experiments in these disciplines provide empirical evidence which drives some of the most important decisions in our society: what drugs are prescribed? Which social programs are implemented? What corporate strategies to use? Technological advances in measurements and intervention -- including high dimensional data, network data, and mobile devices -- offer exciting opportunities to design new experiments to investigate a broader set of causal questions. In these more complex settings, standard experimental designs (e.g. independent assignment of treatment) are far from optimal. Designing experiments which yield the most precise estimates of causal effects in these complex settings is not only a statistical problem, but also an algorithmic one.
In this talk, I will present my recent work on designing algorithms for randomized experiments. I will begin by presenting Clip-OGD, a new algorithmic experimental design for adaptive sequential experiments. We show that under the Clip-OGD design, the variance of an adaptive version of the Horvitz-Thompson estimator converges to the optimal non-adaptive variance, resolving a 70-year-old problem posed by Robbins in 1952. Our results are facilitated by drawing connections to regret minimization in online convex optimization. Time permitting, I will describe a new unifying framework for investigating causal effects under interference, where treatment given to one subject can affect the outcomes of other subjects. Finally, I will conclude by highlighting open problems and reflecting on future work in these directions.
Bio: Christopher Harshaw is a FODSI (Foundations of Data Science) postdoc at MIT and UC Berkeley. He received his PhD from Yale University where he was advised by Dan Spielman and Amin Karbasi. His research lies at the interface of causal inference, machine learning, and algorithm design, with a particular focus on the design and analysis of randomized experiments. His work has appeared in the Journal of the American Statistical Association, Electronic Journal of Statistics, ICML, NeurIPS, and won Best Paper Award at the NeurIPS 2022 workshop, CML4Impact.
Seminar Date/Time: Friday, January 12th, 11:00am
Location: MSB 1147