The Proximal Surrogate Index: Long-Term Treatment Effects under Unobserved Confounding

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Autori principali: Hung, Ting-Chih, Chen, Yu-Chang
Natura: Preprint
Pubblicazione: 2026
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author Hung, Ting-Chih
Chen, Yu-Chang
author_facet Hung, Ting-Chih
Chen, Yu-Chang
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2601_17712
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle The Proximal Surrogate Index: Long-Term Treatment Effects under Unobserved Confounding
Hung, Ting-Chih
Chen, Yu-Chang
Econometrics
Methodology
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.
title The Proximal Surrogate Index: Long-Term Treatment Effects under Unobserved Confounding
topic Econometrics
Methodology
url https://arxiv.org/abs/2601.17712