Combining Multiple Observational Data Sources to Estimate Causal Effects.

ABSTRACT: The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with supplementary information on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general. However, by leveraging the information in the big main data in a principled way, we can improve the estimation efficiencies yet preserve the consistencies of the initial estimators based solely on the validation data. Our framework applies to asymptotically normal estimator

SUBMITTER: Yang S 

PROVIDER: S-EPMC7571608 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature