Susan Athey, Raj Chetty, Guido Imbens, Hyunseung Kang
arXiv 30 Mar 2016 · Statistics — Methodology · 47 citations (OpenAlex)
arXiv:1603.09326 · PDF · DOI · OpenAlex · Extracted main text
Estimating the long-term effects of treatments is of interest in many fields. A common challenge in estimating such treatment effects is that long-term outcomes are unobserved in the time frame needed to make policy decisions. One approach to overcome this missing data problem is to analyze treatments effects on an intermediate outcome, often called a statistical surrogate, if it satisfies the condition that treatment and outcome are independent conditional on the statistical surrogate. The validity of the surrogacy condition is often controversial. Here we exploit that fact that in modern datasets, researchers often observe a large number, possibly hundreds or thousands, of intermediate outcomes, thought to lie on or close to the causal chain between the treatment and the long-term outcome of interest. Even if none of the individual proxies satisfies the statistical surrogacy criterion by itself, using multiple proxies can be useful in causal inference. We focus primarily on a setting with two samples, an experimental sample containing data about the treatment indicator and the surrogates and an observational sample containing information about the surrogates and the primary outcome. We state assumptions under which the average treatment effect be identified and estimated with a high-dimensional vector of proxies that collectively satisfy the surrogacy assumption, and derive the bias from violations of the surrogacy assumption, and show that even if the primary outcome is also observed in the experimental sample, there is still information to be gained from using surrogates.
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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 | Jiafeng Chen \ David M Ritzwoller (2023) Semiparametric estimation of long-term treatment effects | 1.000 | 6 | 3 | 100% |
| 2 | NE Day \ SW Duffy (1996) Trial design based on surrogate end points – application to comparison of different breast screening frequencies | 1.000 | 5 | 4 | 100% |
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| 5 | Ross L Prentice (1989) Surrogate endpoints in clinical trials: definition and operational criteria | 0.874 | 5 | 2 | 100% |
| 6 | Donald B Rubin (1976) Inference and missing data | 0.874 | 5 | 2 | 100% |
| 7 | Constantine E Frangakis \ Donald B Rubin (2002) Principal stratification in causal inference | 0.843 | 3 | 3 | 100% |
| 8 | Whitney K Newey (1990) Semiparametric efficiency bounds | 0.811 | 4 | 2 | 100% |
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| 10 | Colin B Begg \ Denis HY Leung (2000) On the use of surrogate end points in randomized trials | 0.737 | 3 | 2 | 100% |
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