4/18/24 | 4:15pm | E51-376

Raaz Dwivedi

Assistant Professor
Cornell

Abstract: Latent factor models are widely utilized for causal inference in panel data, involving multiple measurements across various units. Popular inference methods include matrix completion for estimating the average treatment effect (ATE) and the nearest neighbor approach for individual treatment effects (ITE). However, these methods respectively underperform with non-low-rank outcomes or when faced with diverse units in the data. To tackle these challenges, we integrate double robustness principles with factor models, introducing estimators designed to be resilient against such issues. We present a doubly robust matrix completion strategy for ATE, capable of ensuring consistency despite unobserved confounding, either with low-rank outcome matrices or propensity matrices, and providing superior error/confidence intervals when both matrices are low-rank. Next, we propose a doubly robust nearest neighbor method for ITE, designed to achieve consistent estimates in the presence of either similar units or measurements, with improved error/confidence intervals when both conditions are met.

Based on:

[1] Doubly robust inference in causal latent factor models https://arxiv.org/abs/2402.11652   

[2] Doubly robust nearest neighbors in factor models https://arxiv.org/abs/2211.14297 

Bio: Raaz Dwivedi joined Department of Operations Research and Information Engineering and Cornell Tech at Cornell University as an Assistant Professor in Jan 2024. Prior to that, he visited Cornell ORIE in Fall 2023 and spent two years as a FODSI postdoc fellow at Harvard and MIT LIDS, and spent a summer at Microsoft Research New England. He did his Ph. D. In EECS at UC Berkeley in 2021 and bachelors in EE at IIT Bombay in 2014. His research builds statistically and computationally efficient strategies for personalized decision-making with theory and methods spanning the areas of causal inference, reinforcement learning, and distribution compression. He has won a best student paper award for work on optimal compression and teaching awards at Harvard and UC Berkeley, and the President of India Gold Medal at IIT Bombay.