Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population

Generalizability is a perennial concern in randomized studies. While randomized studies are the gold standard for establishing causality, study samples are rarely representative of broader populations due to factors such as convenience sampling, participant self-selection, and researchers' inclusion or exclusion criteria. To strengthen the generalizability of randomized experiments, the framework of causal effect generalizability provides a solution. However, existing methods require accessing representative individual-level data from the target population, which is often unavailable due to limited resources, data access restrictions, or privacy concerns. In this paper, we develop a novel method to generalize causal effects using only summary statistics on covariates from the target population. We illustrate the estimator using a real-world study by generalizing the impact of a climate change behavioral intervention from the study sample to a broader population. By avoiding the need for individual-level data from the target population, our method offers a practical tool for generalizing causal findings from randomized studies. We hope that the proposed method helps build more accurate theories and enhance the policy relevance of behavioral and psychological research.