Doubly robust nearest neighbors in factor models

Wednesday, April 12, 2023 - 4:00pm to 4:30pm

Event Calendar Category

LIDS & Stats Tea

Speaker Name

Raaz Dwivedi

Building and Room Number

LIDS Lounge

Abstract

We introduce an improved variant of nearest neighbors for counterfactual inference in panel data settings where multiple units are assigned multiple treatments over multiple time points, each sampled with constant probabilities. We call this estimator a doubly robust nearest neighbor estimator and provide a high probability non-asymptotic error bound for the mean parameter corresponding to each unit at each time. Our guarantee shows that the doubly robust estimator provides a (near-)quadratic improvement in the error compared to nearest neighbor estimators analyzed in prior work for these settings.

Biography

Raaz Dwivedi is currently a FODSI postdoc fellow advised by Prof. Susan Murphy and Prof. Devavrat Shah in CS and Statistics, Harvard and EECS, MIT respectively. He will be starting as an assistant professor in Operations Research and Information Engineering, Cornell University, at Cornell Tech in New York City in Spring 2024. He earned his Ph. D. at EECS, UC Berkeley, advised by Prof. Martin Wainwright and Prof. Bin Yu; and his bachelor's degree at EE, IIT Bombay, advised by Prof. Vivek Borkar. His research builds statistically and computationally efficient strategies for personalized decision-making with theory and methods spanning the areas of causal inference, reinforcement learning, random sampling, and high-dimensional statistics. He won the President of India Gold Medal at IIT Bombay, the Berkeley Fellowship, teaching awards at UC Berkeley and Harvard, and a best student paper award for his work on optimal compression.