Souhardya Sengupta (Harvard)

Title

Low-rank Covariate Balancing Estimators under Interference

Abstract

In observational studies, causal effect estimation becomes difficult when the outcome of one unit depends on the treatments of many other units. Existing approaches rely on inverse probability weighting (IPW) with estimated propensity scores, which can lead to misspecification bias especially when treatment uptake exhibits complex dependencies across units. To address this challenge, we propose a general class of covariate balancing estimators under interference. Existing balancing estimators are limited to settings without spillover effects. We develop our approach under the assumed low-rank structure of potential outcomes, which encompasses a broad class of commonly used assumptions, including anonymous, nearest neighbor, and additive interference, while allowing for more complex, study-specific forms of dependence. We show that the proposed balancing estimators are consistent and asymptotically normal. We also develop an asymptotically valid confidence interval and a data-driven procedure for selecting among candidate low-rank structures. Simulation and empirical studies demonstrate that the proposed estimator is substantially more efficient than the standard IPW estimator.