10 citations · 20 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Doubly Robust Inference in Causal Latent Factor Models
Alberto Abadie, Anish Agarwal, Raaz Dwivedi +1
This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. Th…
Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration
Daniel Ngo, Keegan Harris, Anish Agarwal +2
Synthetic control methods (SCMs) are a canonical approach used to estimate treatment effects from panel data in the internet economy. We shed light on a frequently overlooked but u…
Causal Matrix Completion
Anish Agarwal, Munther Dahleh, Devavrat Shah +1
Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are "missi…
Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave?
Anish Agarwal, Abdullah Alomar, Arnab Sarker +3
As we reach the apex of the COVID-19 pandemic, the most pressing question facing us is: can we even partially reopen the economy without risking a second wave? We first need to und…