Doubly Robust Estimation of Treatment Effects in Staggered Difference-in-Differences with Time-Varying Covariates

Yuhao Deng and Le Kang

Papers from arXiv.org

Abstract: With staggered adoption, difference-in-differences (DiD) analyses define the average treatment effect on the treated (ATT) at the group-period level and aggregate it into groupwise, periodwise, dynamic, and overall effects. We study a period-by-period version of conditional parallel trends, with covariates unaffected by treatment while allowing covariates to shift the trends in untreated potential outcomes. We propose an augmented inverse probability weighting (AIPW) estimator and an augmented inverse variance weighting (AIVW) generalization. Both are doubly robust and asymptotically normal, with closed-form influence-function-based inference for all group-period and aggregated ATTs. AIVW is statistically efficient in some special cases, while AIPW is computationally more efficient. We illustrate the methods with the staggered provincial adoption of a parallel college admission mechanism in China and its effect on justified envy.

Date: 2026-03, Revised 2026-09
New Economics Papers: this item is included in nep-cna and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://arxiv.org/pdf/2603.04080 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: https://EconPapers.repec.org/RePEc:arx:papers:2603.04080

Access Statistics for this paper

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