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When estimating treatment effects, the golden standard is to conduct arandomized experiment and then contrast outcomes associated with the treatmentgroup and the control group. However, in many cases, randomized experiments areeither conducted with a much smaller scale compared to the size of the targetpopulation or accompanied with certain ethical issues and thus hard toimplement. Therefore, researchers usually rely on observational data to studycausal connections. The downside is that the unconfoundedness assumption, thekey to validate the use of observational data is hard to verify and almostalways violated. Hence, any conclusion drawn from observational data should befurther analyzed with great care. Given the richness of observational data andusefulness of experimental data, researchers hope to develop credible method tocombine the strength of the two. In this paper, we consider a setting where theobservational data contain the outcome of interest as well as a surrogateoutcome while the experimental data contain only the surrogate outcome. Wepropose a simple estimator to estimate the average treatment effect of interestusing both the observational data and the experimental data.

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