Propose a strong causal research design, which can reliably and validly isolate the treatment efect of a policy, practice, or intervention. Examples of such research designs include diference-in …

Recent Findings AI tools relevant to causal research in epidemiology include predictive models, unsupervised learning, causal structure learning, causal estimation, and generative models. …

To identify causal mechanisms, the most common approach taken by applied researchers is what we call the single-experiment design where causal mediation analysis is applied to a stan-dard …

Given the crucial importance of causal assumptions, all contributions to this special issue, entitled “Causal Research Designs and Analysis in Education,” high-light the assumptions about the …

And yet in other research fields, such as epidemiology, the emphasis on causal explanation versus empirical prediction is more mixed. Statistical modeling for description, where the …

The critical point is thus not whether a research design hinges on additional assumptions, but which assump-tions need to be made. Regardless of the research design, awareness and …

While causal validity is not the only measure of the usefulness of research evidence, it is the foundation upon which useful evidence is built. The value of research evidence is also affected …