Author

Devavrat Shah

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Preprint Sep 2026

Synthetic Nearest Neighbors: Extending Synthetic Controls for Matrix Completion with Missing Not at Random Data

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 relaxes two assumptions common in MNAR matrix completion: positivity and independence of observation indicators. Unlike traditional panel da...

Anish Agarwal, M. Dahleh, Devavrat Shah et al. · 0 citations

#machine learning Preprint Sep 2026

Causal Inference with Unobserved Confounding: A Mixture Learning Perspective

Unobserved confounding is a fundamental challenge in causal inference from observational data. This article develops a mixture-learning perspective, viewing latent confounders as sources of heterogeneity that induce mixture structure in observed data. Under suitable structural and identifiability assumptions, recoverin...

Mansi Sood, Devavrat Shah · 0 citations

Jul 2026

Causal Inference for Sequential Settings under Interference and Latent Confounding

We study causal inference under outcome interference for sequential, observational settings. Specifically, we consider settings where the binary outcomes over N units are Markovian across T time steps. At each time step, the outcomes of N units have dependencies captured through an Ising model; each outcome is also imp...

Phevos Paschalidis, Constantinos Daskalakis, Devavrat Shah · arXiv.org · 0 citations