arXiv 25 Jan 2026 · Econometrics
arXiv:2601.17712 · PDF · DOI · OpenAlex · Extracted main text
We study the identification and estimation of long-term treatment effects under unobserved confounding by combining an experimental sample, where the long-term outcome is missing, with an observational sample, where the treatment assignment is unobserved. While standard surrogate index methods fail when unobserved confounders exist, we establish novel identification results by leveraging proxy variables for the unobserved confounders. We further develop multiply robust estimation and inference procedures based on these results. Applying our method to the Job Corps program, we demonstrate its ability to recover experimental benchmarks even when unobserved confounders bias standard surrogate index estimates.
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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 | Imbens, Guido and Kallus, Nathan and Mao, Xiaojie and Wang, Yuhao (2025) Long-Term Causal Inference under Persistent Confounding via Data Combination | 1.000 | 6 | 3 | 100% |
| 2 | Cui, Yifan and Pu, Hongming and Shi, Xu and Miao, Wang and Tchetgen… (2024) Semiparametric Proximal Causal Inference | 0.928 | 4 | 4 | 100% |
| 3 | Tchetgen Tchetgen, Eric J. and Ying, Andrew and Cui, Yifan and Shi,… (2024) An Introduction to Proximal Causal Inference | 0.928 | 4 | 3 | 100% |
| 4 | Susan Athey and Raj Chetty and Guido Imbens and Hyunseung Kang (2025) The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely | 0.888 | 20 | 6 | 70% |
| 5 | Miao, Wang and Geng, Zhi and Tchetgen Tchetgen, Eric J (2018) Identifying Causal Effects with Proxy Variables of an Unmeasured Confounder | 0.843 | 3 | 3 | 100% |
| 6 | Ghassami, AmirEmad and Yang, Alan and Richardson, David and Shpitser… (2022) Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects | 0.737 | 3 | 2 | 100% |
| 7 | Chen, Hua and Geng, Zhi and Jia, Jinzhu (2007) Criteria for surrogate end points | 0.644 | 2 | 2 | 100% |
| 8 | Frangakis, Constantine E and Rubin, Donald B (2002) Principal stratification in causal inference | 0.644 | 2 | 2 | 100% |
| 9 | Imbens, Guido W. and Angrist, Joshua D Identification and Estimation of Local Average Treatment Effects | 0.644 | 2 | 2 | 100% |
| 10 | Guido W. Imbens (2014) Instrumental Variables: An Econometrician's Perspective | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 55 scored citations.