arXiv 4 Mar 2026 · Statistics — Methodology
arXiv:2603.04080 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Callaway, Brantly and Sant'Anna, Pedro HC (2021) Difference-in-differences with multiple time periods | 0.874 | 5 | 2 | 100% |
| 2 | Caetano, Carolina and Callaway, Brantly (2024) Difference-in-Differences with Time-Varying Covariates in the Parallel Trends Assumption | 0.644 | 2 | 2 | 100% |
| 3 | Chen, Xiaohong and Sant'Anna, Pedro HC and Xie, Haitian (2025) Efficient Difference-in-Differences and Event Study Estimators | 0.644 | 2 | 2 | 100% |
| 4 | Sun, Liyang and Abraham, Sarah (2021) Estimating dynamic treatment effects in event studies with heterogeneous treatment effects | 0.644 | 2 | 2 | 100% |
| 5 | Abdulkadiro glu, Atila and Sönmez, Tayfun (2003) School choice: A mechanism design approach | 0.511 | 2 | 1 | 100% |
| 6 | Athey, Susan and Imbens, Guido W (2006) Identification and inference in nonlinear difference-in-differences models | 0.511 | 2 | 1 | 100% |
| 7 | Athey, Susan and Imbens, Guido W (2022) Design-based analysis in difference-in-differences settings with staggered adoption | 0.511 | 2 | 1 | 100% |
| 8 | De Chaisemartin, Clément and d’Haultfoeuille, Xavier (2020) Two-way fixed effects estimators with heterogeneous treatment effects | 0.511 | 2 | 1 | 100% |
| 9 | Goodman-Bacon, Andrew (2021) Difference-in-differences with variation in treatment timing | 0.511 | 2 | 1 | 100% |
| 10 | Kang, Le and Ha, Wei and Song, Yang and Zhou, Sen (2020) Matching mechanisms, justified envy, and college admissions outcomes self | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 36 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Causal Graphs for Conditional Parallel Trends | 0.000 | 1 | 1 |