arXiv 7 Jun 2026 · Econometrics
arXiv:2606.08474 · PDF · DOI · OpenAlex · Extracted main text
This paper considers a semiparametric difference-in-differences (DID) framework for identifying and estimating treatment effects on the treated (ATT) when outcomes are missing not at random (MNAR), and a fully observed shadow variable is available. The shadow variable is assumed to be associated with the outcome evolution but independent of the missingness process, conditional on covariates and the possibly unobserved outcome evolution. We establish the identification conditions, derive the corresponding identification results and estimation algorithm, and evaluate the finite-sample performance of the proposed estimator through simulation studies and a real data application.
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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 | Miao, Wang and Liu, Lan and Li, Yilin and Tchetgen Tchetgen, Eric J… (2024) Identification and semiparametric efficiency theory of nonignorable missing data with a shadow variable | 0.928 | 4 | 3 | 100% |
| 2 | Zhao, Jiwei and Ma, Yanyuan (2022) A versatile estimation procedure without estimating the nonignorable missingness mechanism | 0.928 | 4 | 3 | 100% |
| 3 | Deng, Xin and Yu, Mingzhe (2021) Does the marginal child increase household debt?–Evidence from the new fertility policy in China | 0.874 | 9 | 2 | 100% |
| 4 | Li, Junjie and Matsushita, Yukitoshi (2025) A difference-in-differences estimator by covariate balancing propensity score self | 0.843 | 3 | 3 | 100% |
| 5 | Miao, Wang and Tchetgen Tchetgen, Eric J (2016) On varieties of doubly robust estimators under missingness not at random with a shadow variable | 0.811 | 4 | 2 | 100% |
| 6 | Shin, Sooahn (2024) Difference-in-differences Design with Outcomes Missing Not at Random | 0.737 | 4 | 3 | 50% |
| 7 | Abadie, Alberto (2005) Semiparametric difference-in-differences estimators | 0.644 | 2 | 2 | 100% |
| 8 | Chang, Neng-Chieh (2020) Double/debiased machine learning for difference-in-differences models | 0.644 | 2 | 2 | 100% |
| 9 | d’Haultfoeuille, Xavier (2010) A new instrumental method for dealing with endogenous selection | 0.644 | 2 | 2 | 100% |
| 10 | Sant’Anna, Pedro HC and Zhao, Jun (2020) Doubly robust difference-in-differences estimators | 0.644 | 2 | 2 | 100% |
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