Jinghao Sun, Eric J. Tchetgen Tchetgen
arXiv 9 Jul 2025 · Statistics — Methodology
arXiv:2507.07228 · PDF · DOI · OpenAlex · Extracted main text
We present a novel extension of the influential changes-in-changes (CiC) framework of Athey and Imbens (2006) for estimating the average treatment effect on the treated (ATT) and distributional causal effects in panel data with unmeasured confounding. While CiC relaxes the parallel trends assumption in difference-in-differences (DiD), existing methods typically assume a scalar unobserved confounder and monotonic outcome relationships, and lack inference tools that accommodate continuous covariates flexibly. Motivated by empirical settings with complex confounding and rich covariate information, we make two main contributions. First, we establish nonparametric identification under relaxed assumptions that allow high-dimensional, non-monotonic unmeasured confounding. Second, we derive semiparametrically efficient estimators that are Neyman orthogonal to infinite-dimensional nuisance parameters, enabling valid inference even with machine learning-based estimation of nuisance components. We illustrate the utility of our approach in an empirical analysis of mass shootings and U.S. electoral outcomes, where key confounders, such as political mobilization or local gun culture, are typically unobserved and challenging to quantify.
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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 | Susan Athey and Guido W Imbens (2006) Identification and inference in nonlinear difference-in-differences models | 0.928 | 10 | 5 | 80% |
| 2 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 5 | 2 | 60% |
| 3 | Hans JG Hassell and John B Holbein (2025) Navigating potential pitfalls in difference-in-differences designs: Reconciling conflicting findings on mass shootings' effect o… | 0.737 | 3 | 2 | 100% |
| 4 | Hasin Yousaf (2021) Sticking to one's guns: Mass shootings and the political economy of gun control in the United States | 0.737 | 3 | 2 | 100% |
| 5 | Sergio Firpo (2007) Efficient semiparametric estimation of quantile treatment effects | 0.644 | 3 | 2 | 67% |
| 6 | Andrew Baker, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bac… (2025) Difference-in-differences designs: A practitioner's guide | 0.644 | 2 | 2 | 100% |
| 7 | Brantly Callaway and Pedro H.C. Sant'Anna (2021) did: Difference in differences, 2021a | 0.644 | 2 | 2 | 100% |
| 8 | Miguel A Hernán and James M Robins (2020) Causal Inference: What If | 0.644 | 2 | 2 | 100% |
| 9 | Jillian K Peterson, James A Densley, Molly Hauf, and Jack Moldenhauer (2024) Epidemiology of mass shootings in the United States | 0.644 | 2 | 2 | 100% |
| 10 | Hasin Yousaf (2022) Replication Data for: Sticking to One's Guns: Mass Shootings and the Political Economy of Gun Control in the United States, 2022 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Asymptotic Properties of Empirical Quantile-Based Estimators | 0.405 | 1 | 1 |