Title: Robust estimation of causal effects under data combination using instrumental variables
Authors: BaoLuo Sun - National University of Singapore (Singapore) [presenting]
Abstract: Although instrumental variable (IV) methods are widely used to estimate causal effects in the presence of unmeasured confounding, the IVs, exposure and outcome are often not measured in the same sample due to complex data harvesting procedures. Following previous influential articles, numerous empirical researchers have applied two-sample IV methods to perform joint estimation based on an IV-exposure sample and an IV-outcome sample. We develop a general semi-parametric framework for two-sample data combination models and propose new multiply robust locally efficient estimators of the causal effect of exposure on the outcome, and illustrate the methods through simulation and an econometric application on public housing projects.