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

2026-03-04

AI Summary

This study addresses the bias arising from negative weights in staggered difference-in-differences (DiD) designs, which distorts the average of heterogeneous treatment effects—particularly when time-varying covariates are present. Within a model-agnostic framework, the paper nonparametrically defines group-time, group, period, and dynamic average treatment effects for the first time. To this end, the authors propose an augmented inverse variance-weighted (AIVW) estimator that integrates the augmented inverse probability weighting (AIPW) principle, achieving double robustness: consistent estimation of target parameters is maintained even if either the outcome or propensity score model is misspecified. The asymptotic variance is derived via influence functions, enabling valid causal inference with time-varying covariates. Simulations and an empirical application to China’s college admissions policy demonstrate that the estimator performs reliably in finite samples and effectively identifies the causal effects of parallel志愿 and immediate enrollment policies.

Technology Category

Humans and AI: VotingMachine Learning: Causal LearningMultiagent Systems: Mechanism Design

Application Category

User Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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.

Problem

• staggered difference-in-differences
• negative weights
• time-varying covariates
• treatment effect estimation
• heterogeneous effects

Innovation

• Doubly Robust Estimation
• Staggered Difference-in-Differences
• Time-Varying Covariates
• Augmented Inverse Variance Weighting
• Average Treatment Effect on the Treated