Estimating Treatment Effects Using Observational Data and Experimental Data with Non-overlapping Support

TL;DR

This paper proposes a method to estimate treatment effects by combining observational data with experimental data that only contains surrogate outcomes, addressing challenges of unconfoundedness and non-overlapping support.

Contribution

It introduces a simple estimator that leverages both data sources to improve causal effect estimation when experimental data lacks the primary outcome.

Findings

Estimator effectively combines observational and experimental data.

Method improves accuracy of treatment effect estimates.

Addresses issues of unconfoundedness and support overlap.

Abstract

When estimating treatment effects, the golden standard is to conduct a randomized experiment and then contrast outcomes associated with the treatment group and the control group. However, in many cases, randomized experiments are either conducted with a much smaller scale compared to the size of the target population or accompanied with certain ethical issues and thus hard to implement. Therefore, researchers usually rely on observational data to study causal connections. The downside is that the unconfoundedness assumption, the key to validate the use of observational data is hard to verify and almost always violated. Hence, any conclusion drawn from observational data should be further analyzed with great care. Given the richness of observational data and usefulness of experimental data, researchers hope to develop credible method to combine the strength of the two. In this paper, we consider a setting where the observational data contain the outcome of interest as well as a surrogate outcome while the experimental data contain only the surrogate outcome. We propose a simple estimator to estimate the average treatment effect of interest using both the observational data and the experimental data.