11 citations · 25 across the 6 of their papers we have counts for
7 papers
On Causal Inference with Model-Based Outcomes
Dmitry Arkhangelsky, Kazuharu Yanagimoto, Tom Zohar
We study the estimation of causal effects on group-level parameters identified from microdata (e.g., child penalties). We demonstrate that standard one-step methods (such as pooled…
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…
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…
Design-Robust Two-Way-Fixed-Effects Regression For Panel Data
Dmitry Arkhangelsky, Guido W. Imbens, Lihua Lei +1
We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns. Our approach augments the popular two-way-f…
Doubly Robust Identification for Causal Panel Data Models
Dmitry Arkhangelsky, Guido W. Imbens
We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions…
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…
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…