From Experiments to Policy Insights: Generalizing Causal Effects From Study Samples to Target Populations

Psychological science holds substantial promise for informing policy decisions but faces challenges in realizing its potential. One widely recognized challenge is bridging the gap between the nonrepresentative study samples commonly used to evaluate interventions and the broader populations that policymakers aim to serve. To address this challenge, we introduce causal effect generalizability, an approach from causal inference and epidemiology, in the form of an accessible, nontechnical tutorial for psychological and behavioral scientists. We use publicly available data from a real-world psychology intervention study to illustrate why causal effects in a nonrepresentative study sample may systematically differ from those in a broader population. We provide a step-by-step guide with user-friendly R functions, enabling researchers to generalize causal effects from a study sample back to the full target population. This approach allows researchers to assess intervention effects in broader populations, offering valuable insights to guide evidence-based policy development. We hope this nontechnical introductory material will assist scholars in enhancing the policy relevance and real-world impact of psychological science.