8 citations · 13 across the 8 of their papers we have counts for
4 papers · 1 filter
Causal Inference with Unobserved Confounding: A Mixture Learning Perspective
Mansi Sood, Devavrat Shah
Unobserved confounding is a fundamental challenge in causal inference from observational data. This article develops a mixture-learning perspective, viewing latent confounders as s…
Synthetic Nearest Neighbors: Extending Synthetic Controls for Matrix Completion with Missing Not at Random Data
Anish Agarwal, Munther Dahleh, Devavrat Shah +1
We develop a causal framework for matrix completion under missing not at random (MNAR) data. Drawing on synthetic controls from the econometric panel data literature, our approach…
Causal Inference for Sequential Settings under Interference and Latent Confounding
Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah
We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian…
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…