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…
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…
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…
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…