Yiqi Liu
arXiv 8 Nov 2025 · Econometrics
arXiv:2511.05870 · PDF · Extracted main text
Popular empirical strategies for policy evaluation in the panel data literature -- including difference-in-differences (DID), synthetic control (SC) methods, and their variants -- rely on key identifying assumptions that can be expressed through a specific choice of weights $ω$ relating pre-treatment trends to the counterfactual outcome. While each choice of $ω$ may be defensible in empirical contexts that motivate a particular method, it relies on fundamentally untestable and often fragile assumptions. I develop an identification framework that allows for all weights satisfying a Synthetic Parallel Trends assumption: the treated unit's trend is parallel to a weighted combination of control units' trends for a general class of weights. The framework nests these existing methods as special cases and is by construction robust to violations of their respective assumptions. I construct a valid confidence set for the identified set of the treatment effect, which admits a linear programming representation with estimated coefficients and nuisance parameters that are profiled out. In simulations where the assumptions underlying DID or SC-based methods are violated, the proposed confidence set remains robust and attains nominal coverage, while existing methods suffer severe undercoverage.
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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 | Arkhangelsky, Dmitry and Athey, Susan and Hirshberg, David A and Imb… (2021) Synthetic difference-in-differences | 1.000 | 33 | 5 | 100% |
| 2 | Abadie, Alberto and Diamond, Alexis and Hainmueller, Jens (2010) Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program | 1.000 | 22 | 4 | 100% |
| 3 | Fang, Zheng and Santos, Andres (2019) Inference on directionally differentiable functions | 1.000 | 7 | 3 | 100% |
| 4 | Imbens, Guido W and Viviano, Davide (2023) Identification and Inference for Synthetic Controls with Confounding | 0.928 | 4 | 3 | 100% |
| 5 | Rambachan, Ashesh and Roth, Jonathan (2023) A more credible approach to parallel trends | 0.874 | 9 | 2 | 100% |
| 6 | Ban, Kyunghoon and Kédagni, Désiré (2023) Robust Difference-in-differences Models | 0.737 | 3 | 2 | 100% |
| 7 | Cox, Gregory and Shi, Xiaoxia and Shimizu, Yuya (2025) Testing Inequalities Linear in Nuisance Parameters | 0.737 | 3 | 2 | 100% |
| 8 | Freyberger, Joachim and Horowitz, Joel L (2015) Identification and shape restrictions in nonparametric instrumental variables estimation | 0.737 | 3 | 2 | 100% |
| 9 | Gafarov, Bulat (2025) Simple subvector inference on sharp identified set in affine models | 0.737 | 3 | 2 | 100% |
| 10 | Goff, Leonard and Mbakop, Eric (2025) Inference on the value of a linear program | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 55 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inference for Linear Systems with Unknown Coefficients | 0.874 | 5 | 2 |
| 2 | 2510.26106 | 0.511 | 2 | 1 |
| 3 | Beyond Parallel Trends: An Identification-Strategy-Robust Approach to Causal Inference with Panel Data | 0.405 | 1 | 1 |
| 4 | Robust Inference for Weighted Estimands | 0.405 | 1 | 1 |