25 citations · 100 across the 32 of their papers we have counts for
5 papers · 1 filter
Balanced Linear Contextual Bandits
Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey +1
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity…
Synthetic Difference in Differences
Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg +2
We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods. Relative to th…
Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption
Susan Athey, Guido Imbens
In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or sta…
A Causal Bootstrap
Guido Imbens, Konrad Menzel
The bootstrap, introduced by Efron (1982), has become a very popular method for estimating variances and constructing confidence intervals. A key insight is that one can approximat…
Fixed Effects and the Generalized Mundlak Estimator
Dmitry Arkhangelsky, Guido Imbens
We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity. We consider a general model with group-level…