Description
Randomized Controlled Trials (RCTs) are pivotal in evidence-based medicine, estimating average treatment effects by avoiding confounding factors. However, concerns about RCT limitations—strict eligibility criteria, real-world impracticality, and small sample sizes—threaten their generalization to diverse populations. In this talk, I will first present transportability methods by integrating non-randomized observational data to extend trial findings to other populations, potentially facing distributional shifts. Then, I will focus on which causal measure is easier to generalize, whether absolute as the Risk Difference or relative as the Risk Ratio, Odds Ratio, etc. In particular, I will demonstrate that only the Risk difference can disentangle the treatment effect from the baseline risk at both population and strata levels.