1.6k citations · 2.1k across the 53 of their papers we have counts for
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Identification and Inference for Synthetic Controls with Confounding
Guido W. Imbens, Davide Viviano
This paper studies inference on treatment effects in panel data settings with unobserved confounding. We model outcome variables through a factor model with random factors and load…
Causal Models for Longitudinal and Panel Data: A Survey
Dmitry Arkhangelsky, Guido Imbens
In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with…
Causal clustering: design of cluster experiments under network interference
Davide Viviano, Lihua Lei, Guido Imbens +3
This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. We provide a framework to choose the clustering…
Estimating the Value of Evidence-Based Decision Making
Alberto Abadie, Anish Agarwal, Guido Imbens +4
In an era of data abundance, statistical evidence is increasingly critical for business and policy decisions. Yet, organizations lack empirical tools to assess the value of evidenc…
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
Jann Spiess, Guido Imbens, Amar Venugopal
Motivated by a recent literature on the double-descent phenomenon in machine learning, we consider highly over-parameterized models in causal inference, including synthetic control…
Synthetic Difference In Differences Estimation
Damian Clarke, Daniel Pailañir, Susan Athey +1
In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata. Synthetic difference-…