13 citations · 29 across the 7 of their papers we have counts for
10 papers
Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables
Dmitry Arkhangelsky, David Hirshberg
We analyze the synthetic control (SC) method in panel data settings with many units. We assume the treatment assignment is based on unobserved heterogeneity and pre-treatment infor…
A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
Khiem Pham, David A. Hirshberg, Phuong-Mai Huynh-Pham +3
We propose an empirically stable and asymptotically efficient covariate-balancing approach to the problem of estimating survival causal effects in data with conditionally-independe…
The Balancing Act in Causal Inference
Eli Ben-Michael, Avi Feller, David A. Hirshberg +1
The idea of covariate balance is at the core of causal inference. Inverse propensity weights play a central role because they are the unique set of weights that balance the covaria…
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual Bandits
Ruohan Zhan, Vitor Hadad, David A. Hirshberg +1
It has become increasingly common for data to be collected adaptively, for example using contextual bandits. Historical data of this type can be used to evaluate other treatment as…
Least Squares with Error in Variables
David A. Hirshberg
Error-in-variables regression is a common ingredient in treatment effect estimators using panel data. This includes synthetic control estimators, counterfactual time series forecas…
Confidence Intervals for Policy Evaluation in Adaptive Experiments
Vitor Hadad, David A. Hirshberg, Ruohan Zhan +2
Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known…
Minimax Linear Estimation of the Retargeted Mean
David A. Hirshberg, Arian Maleki, Jose R. Zubizarreta
Evaluating treatments received by one population for application to a different target population of scientific interest is a central problem in causal inference from observational…
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…
Debiased Inference of Average Partial Effects in Single-Index Models
David A. Hirshberg, Stefan Wager
We propose a method for average partial effect estimation in high-dimensional single-index models that is root-n-consistent and asymptotically unbiased given sparsity assumptions o…
Augmented Minimax Linear Estimation
David A. Hirshberg, Stefan Wager
Many statistical estimands can expressed as continuous linear functionals of a conditional expectation function. This includes the average treatment effect under unconfoundedness a…