Doubly robust difference-in-differences estimators

This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, the proposed estimators are consistent if either (but not necessarily both) a propensity score or outco...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of econometrics Jg. 219; H. 1; S. 101 - 122
Hauptverfasser: Sant’Anna, Pedro H.C., Zhao, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.11.2020
Elsevier Science Publishers
Elsevier Sequoia S.A
Schlagworte:
Analysis
Asymptotic methods
Causal inference
computer software
Difference-in-differences
econometric models
Econometrics
economic analysis
economic theory
Efficiency
Estimating techniques
Finite element analysis
Inference
Natural experiments
Panel data
Policy analysis
Propensity
Public software
Regression analysis
Repeated cross-section data
Research design
Semiparametric efficiency
Simulation
Working models
Causal inference
Natural experiments
Semiparametric efficiency
Panel data
Difference-in-differences
Repeated cross-section data
ISSN:0304-4076, 1872-6895
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, the proposed estimators are consistent if either (but not necessarily both) a propensity score or outcome regression working models are correctly specified. We also derive the semiparametric efficiency bound for the ATT in DID designs when either panel or repeated cross-section data are available, and show that our proposed estimators attain the semiparametric efficiency bound when the working models are correctly specified. Furthermore, we quantify the potential efficiency gains of having access to panel data instead of repeated cross-section data. Finally, by paying particular attention to the estimation method used to estimate the nuisance parameters, we show that one can sometimes construct doubly robust DID estimators for the ATT that are also doubly robust for inference. Simulation studies and an empirical application illustrate the desirable finite-sample performance of the proposed estimators. Open-source software for implementing the proposed policy evaluation tools is available.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2020.06.003