Doubly Robust Difference-in-Differences Estimators

Content Provider	Scilit
Author	Sant'Anna, Pedro H. C. Zhao, Jun B.
Copyright Year	2018
Description	Journal: SSRN Electronic Journal This article proposes a doubly robust estimation procedure for the average treatment effect on the treated in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, our proposed estimators are consistent if either (but not necessarily both) a propensity score model or outcome regression models are correctly specified. In addition, our proposed methodology accommodates linear and nonlinear specifications, allows for treatment effect heterogeneity, and can be applied with either panel or repeated cross section data. We establish the asymptotic distribution of our proposed doubly robust estimators, and propose a computationally simple bootstrap procedure to conduct asymptotically valid inference. Our inference procedures directly account for multiple testing, and are therefore suitable in situations where researchers are interested in the effect of a given policy on many different outcomes. We demonstrate the relevance of our proposed policy evaluation tools in two different applications.
Related Links	http://arxiv.org/pdf/1812.01723 https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3293315
ISSN	10914358
e-ISSN	15565068
DOI	10.2139/ssrn.3293315
Journal	SSRN Electronic Journal
Language	English
Publisher	Elsevier BV
Publisher Date	2018-11-29
Access Restriction	Open
Subject Keyword	Journal: SSRN Electronic Journal Statistics and Probability Causal Inference Multiple Testing Natural Experiments Policy Evaluation Treatment Effects
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health Psychiatry and Mental Health