On the role of surrogates in the efficient estimation of treatment effects with limited outcome data

TL;DR

This paper explores how using surrogate outcomes can improve the precision of treatment effect estimates in studies with limited primary outcome data, without strict surrogacy assumptions.

Contribution

It introduces a framework for incorporating surrogate data to enhance ATE estimation, deriving efficiency bounds and developing robust methods without strong surrogacy assumptions.

Findings

Surrogates can significantly increase estimation precision.

Efficiency bounds show potential gains from surrogate data.

Empirical application demonstrates practical benefits.

Abstract

In many experimental and observational studies, the outcome of interest is often difficult or expensive to observe, reducing effective sample sizes for estimating average treatment effects (ATEs) even when identifiable. We study how incorporating data on units for which only surrogate outcomes not of primary interest are observed can increase the precision of ATE estimation. We refrain from imposing stringent surrogacy conditions, which permit surrogates as perfect replacements for the target outcome. Instead, we supplement the available, albeit limited, observations of the target outcome with abundant observations of surrogate outcomes, without any assumptions beyond unconfounded treatment assignment and missingness and corresponding overlap conditions. To quantify the potential gains, we derive the difference in efficiency bounds on ATE estimation with and without surrogates, both when an overwhelming or comparable number of units have missing outcomes. We develop robust ATE estimation and inference methods that realize these efficiency gains. We empirically demonstrate the gains by studying long-term-earning effects of job training.