Yanqin Fan, Carlos A. Manzanares, Hyeonseok Park, Yuan Qi
arXiv 28 Feb 2026 · Econometrics
arXiv:2603.00580 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a sensitivity analysis of the surrogacy assumption for the surrogate index approach in Athey et al. [2025b]. We introduce "Weighted Surrogate Indices (WSIs)," the analog of the surrogate index under the surrogacy assumption. We show that under comparability, the ATE on WSI identifies the ATE on the long-term outcome when a copula of the treatment and the long-term outcome conditional on baseline covariates and surrogates is known. When the copula is unknown, we establish the identified set of the ATE on the long-term outcome. Furthermore, we construct debiased estimators of the ATE for any given copula and develop asymptotically valid inference in both point-identified and partially identified cases. Using data from a poverty alleviation program in Pakistan, we demonstrate the importance of sensitivity checks as well as the usefulness of our approach.
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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, Raj Chetty, Guido W Imbens, and Hyunseung Kang (2025) The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects more Rapidly and Precisely | 0.983 | 41 | 7 | 95% |
| 2 | Jiafeng Chen and David M. Ritzwoller (2023) Semiparametric estimation of long-term treatment effects | 0.965 | 10 | 3 | 90% |
| 3 | Jacob Dorn, Kevin Guo, and Nathan Kallus (2024) Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding | 0.822 | 9 | 3 | 56% |
| 4 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.763 | 18 | 4 | 44% |
| 5 | Alois Pichler (2013) Premiums and reserves, adjusted by distortions | 0.737 | 3 | 3 | 67% |
| 6 | Abhijit Banerjee, Esther Duflo, Nathanael Goldberg, Dean Karlan, Rob… (2015) A multifaceted program causes lasting progress for the very poor: Evidence from six countries | 0.737 | 3 | 2 | 100% |
| 7 | Vira Semenova (2025) Generalized Lee bounds | 0.737 | 3 | 2 | 100% |
| 8 | Stamatis Cambanis, Gordon Simons, and William Stout (1976) Inequalities for E k(X, Y) when the marginals are fixed | 0.644 | 2 | 2 | 100% |
| 9 | Jeremy Yang, Dean Eckles, Paramveer Dhillon, and Sinan Aral (2023) Targeting for Long-Term Outcomes | 0.644 | 2 | 2 | 100% |
| 10 | Tomasz Olma (2021) Nonparametric Estimation of Truncated Conditional Expectation Functions, September 2021 | 0.511 | 4 | 2 | 25% |
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