Economics at your fingertips  

Doubly Robust Difference-in-Differences Estimators

Pedro Sant'Anna () and Jun B. Zhao

Papers from

Abstract: 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.

New Economics Papers: this item is included in nep-ecm
Date: 2018-11
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Papers from
Bibliographic data for series maintained by arXiv administrators ().

Page updated 2019-05-07
Handle: RePEc:arx:papers:1812.01723