Surrogate Outcomes and Transportability

TL;DR

This paper introduces surrogate outcome identifiability, a generalization of causal effect identification using surrogate experiments, and shows how transportability criteria can determine identifiability in complex causal inference scenarios.

Contribution

It extends causal identifiability theory by integrating surrogate outcomes and transportability, providing new criteria for identifying causal effects when direct data is insufficient.

Findings

Transportability criteria can determine surrogate outcome identifiability.

Surrogate outcomes enable causal effect identification with limited data.

The approach generalizes existing identifiability methods.

Abstract

Identification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is sufficient for the identification of the causal effect of interest. Instead of the outcome of interest, surrogate outcomes are measured in the experiments. This problem is a generalization of identifiability using surrogate experiments and we label it as surrogate outcome identifiability. We show that the concept of transportability provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.