Yixiao Sun, Haitian Xie, Yuhang Zhang
arXiv 14 Mar 2025 · Econometrics
arXiv:2503.11375 · PDF · DOI · OpenAlex · Extracted main text
Difference-in-Differences (DiD) and Synthetic Control (SC) are widely used methods for causal inference in panel data, each with distinct strengths and limitations. We propose a novel method for short-panel causal inference that integrates the advantages of both approaches. Our method delivers a doubly robust identification strategy for the average treatment effect on the treated (ATT) under either of two non-nested assumptions: parallel trends or a group-level SC condition. Building on this identification result, we develop a unified semiparametric framework for estimating the ATT. Notably, the identification-robust moment function satisfies Neyman orthogonality under the parallel trends assumption but not under the SC assumption, leading to different asymptotic variances across the two identification strategies. To ensure valid inference, we propose a multiplier bootstrap method that consistently approximates the asymptotic distribution under either assumption. Furthermore, we extend our methodology to accommodate repeated cross-sectional data and staggered treatment designs. As an empirical application, we evaluate the impact of the 2003 minimum wage increase in Alaska on family income. Finally, in simulation studies based on empirically calibrated data-generating processes, we demonstrate that the proposed estimation and inference methods perform well in finite samples under either identification assumption.
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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 | Gunsilius, F. F (2023) Distributional synthetic controls | 1.000 | 14 | 3 | 100% |
| 2 | Sant’Anna, P. H. and J. Zhao (2020) Doubly robust difference-in-differences estimators | 1.000 | 6 | 4 | 100% |
| 3 | Callaway, B. and P. H. Sant’Anna (2021) Difference-in-differences with multiple time periods | 0.928 | 4 | 3 | 100% |
| 4 | Abadie, A (2005) Semiparametric difference-in-differences estimators | 0.843 | 3 | 3 | 100% |
| 5 | Arkhangelsky, D., S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wa… (2021) Synthetic difference-in-differences | 0.737 | 3 | 2 | 100% |
| 6 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
| 7 | Dube, A (2019) Minimum wages and the distribution of family incomes | 0.693 | 7 | 1 | 100% |
| 8 | Ben-Michael, E., A. Feller, and J. Rothstein (2021) The augmented synthetic control method | 0.644 | 2 | 2 | 100% |
| 9 | Robbins, M. W., J. Saunders, and B. Kilmer (2017) A framework for synthetic control methods with high-dimensional, micro-level data: Evaluating a neighborhood-specific crime inte… | 0.644 | 2 | 2 | 100% |
| 10 | Chen, X., O. Linton, and I. Van Keilegom (2003) Estimation of semiparametric models when the criterion function is not smooth | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Synthetic Parallel Trends | 0.737 | 3 | 2 |
| 2 | 2510.26106 | 0.693 | 5 | 1 |
| 3 | Beyond Parallel Trends: An Identification-Strategy-Robust Approach to Causal Inference with Panel Data | 0.405 | 1 | 1 |