8 citations · 13 across the 8 of their papers we have counts for
9 papers
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…
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…
Personalized Predictions from Population Level Experiments: A Study on Alzheimer's Disease
Dennis Shen, Anish Agarwal, Vishal Misra +4
The purpose of this article is to infer patient level outcomes from population level randomized control trials (RCTs). In this pursuit, we utilize the recently proposed synthetic n…
Estimating Ground Reaction Forces from Inertial Sensors
Bowen Song, Marco Paolieri, Harper E. Stewart +4
Objective: Our aim is to determine if data collected with inertial measurement units (IMUs) during steady-state running could be used to estimate ground reaction forces (GRFs) and…
Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference
Anish Agarwal, Sarah H. Cen, Devavrat Shah +1
We propose a generalization of the synthetic controls and synthetic interventions methodology to incorporate network interference. We consider the estimation of unit-specific poten…
On Multivariate Singular Spectrum Analysis: Tensor and Matrix Variants
Anish Agarwal, Abdullah Alomar, Devavrat Shah
We introduce and analyze two extensions of Singular Spectrum Analysis (SSA) to the multivariate setting: a new variant of the well-known matrix-based method (mSSA), and a novel ten…