3 papers
cs.LG2026
Causal Inference for Sequential Settings under Interference and Latent Confounding
Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah
The paper proposes a method to estimate causal effects in sequential observational data where units influence each other and hidden factors affect outcomes, using an Ising model wi…
stat.ME2026
Causal Inference with Categorical Unobserved Confounder via Mixture Learning
Aytijhya Saha, Stephen Bates, Devavrat Shah
Unobserved confounding is a fundamental challenge for estimating causal effects. To address unobserved confounding, recent literature has turned to two different approaches -- prox…
econ.EM2025
A Causal Inference Framework for Data Rich Environments
Alberto Abadie, Anish Agarwal, Devavrat Shah
We propose a formal model for counterfactual estimation with unobserved confounding in "data-rich" settings, i.e., where there are a large number of units and a large number of mea…