Doubly robust local projections difference-in-differences
Abstract
This paper develops a doubly robust extension of local-projections difference-in-differences (LP-DiD) for staggered absorbing treatments. The resulting estimator, DRLPDID, preserves the LP-DiD local-stack ATT target and is consistent when either the local untreated-outcome regression or the local treatment-probability model is correctly specified. It also delivers influence-function-based inference for post-treatment summaries and multiplier-bootstrap bands for dynamic paths. In Monte Carlo designs with covariate-driven selection, DRLPDID matches regression-adjusted LP-DiD under outcome-model alignment and clearly outperforms the IPT-only variant under propensity-score misspecification. In the no-fault-divorce application, DRLPDID tracks robust staggered-adoption estimators and is less negative than unadjusted LP-DiD.
Cite
@article{arxiv.2604.27035,
title = {Doubly robust local projections difference-in-differences},
author = {Daniel de Abreu Pereira Uhr and Guilherme Valle Moura},
journal= {arXiv preprint arXiv:2604.27035},
year = {2026}
}