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

TL;DR

This paper introduces doubly robust estimators for treatment effects in staggered difference-in-differences designs with time-varying covariates, addressing issues with negative weights and model misspecification.

Contribution

It develops model-free, doubly robust estimators for various ATTs in staggered DiD settings, accommodating time-varying covariates and providing explicit variance calculations.

Findings

Estimators remain consistent even if some models are misspecified.

Simulation studies demonstrate the estimators' desirable properties.

Application to Chinese exam data illustrates practical utility.

Abstract

The difference-in-differences (DiD) design is a quasi-experimental method for estimating treatment effects. In staggered DiD with multiple treatment groups and periods, estimation based on the two-way fixed effects model yields negative weights when averaging heterogeneous group-period treatment effects into an overall effect. To address this issue, we first define group-period average treatment effects on the treated (ATT), and then define groupwise, periodwise, dynamic, and overall ATTs nonparametrically, so that the estimands are model-free. We propose doubly robust estimators for these types of ATTs in the form of augmented inverse variance weighting (AIVW). The proposed framework allows time-varying covariates that partially explain the time trends in outcomes. Even if part of the working models is misspecified, the proposed estimators still consistently estimate the parameter of interest. The asymptotic variance can be explicitly computed from influence functions. Under a homoskedastic working model, the AIVW estimator is simplified to an augmented inverse probability weighting (AIPW) estimator. We demonstrate the desirable properties of the proposed estimators through simulation and an application that compares the effects of a parallel admission mechanism with immediate admission on the China National College Entrance Examination.