This chapter provides an introduction to the problems of generalization and transportation and methods for addressing these concerns. The field of causal inference is one that, at its core, focuses on improving internal validity--the extent to which a study can establish a trustworthy cause-and-effect relationship between a treatment and outcome. To understand potential external validity bias,...
Generalizing causal estimates in randomized experiments to a broader target population is essential for guiding decisions by policymakers and practitioners in the social and biomedical sciences. While recent papers developed various weighting estimators for the population average treatment effect (PATE), many of these methods result in large variance because the experimental sample...
External validity of randomized experiments is a focus of long-standing debates in the social sciences. While the issue has been extensively studied...
Scientists are often interested in generalizing causal effects estimated in an experiment to a target population. However, analysts are often constrained by available covariate information...
Experiments have come to be a widely accepted and highly regarded method for political science research. Randomization allows for well identified causal effects that are "internally valid" to the experimental setting. However, political scientists are driven by asking big questions with broad impacts...
Randomized controlled trials (RCTs) can provide unbiased estimates of sample average treatment effects. However, a common concern is that RCTs may fail to provide unbiased estimates of population average treatment effects. We derive assumptions...