02 A Semiparametric Approach to Data-Integrated Causal Inference

Oregon Convention Center 

In causal inference, data may be collected from multiple sources such as experimental and observational studies. Experimental studies often suffer from the lack of external validity due to the limitation of the studies. Observational studies are usually broad enough to be representative of the target populations, but they often lack internal validity caused by the inevitable uncontrolled confounders. Recently, there has emerged a lot of discussions on integrating experimental and observational studies to make more efficient causal inference. In this poster, we introduce a semiparametric approach based on the density ratio model (DRM) to utilize the complementary features between the two studies. DRM is known for its ability to efficiently account for the latent structures between multiple interconnected populations. If the related studies share some common measurements for the same causal effect, the collected datasets are naturally expected to be from similar and connected populations. Therefore, it is advantageous to jointly analyze these datasets together. We also study several estimators of the causal effect not only from the mean but also from distribution perspectives.