The premise of Medicaid as a policy laboratory rests on generating knowledge through rigorous evaluation. Yet, too often, evaluation has functioned only as a compliance exercise. Instead, it should be ...

Google's TabFM skips per-dataset training and still predicts on unseen tables, matching tuned baselines and cutting pipeline ...

be able to use counterfactuals to express and interpret causal queries; be able to judge when standard statistical methodology is appropriate for causal inference, and when it is not; be able to use ...

Inferring the causes of illness is a culturally universal example of causal thinking. We tested the hypothesis that making causal inferences about biological processes (e.g. illness) depends on the ...

Please join the JHU CFAR Biostatistics and Epidemiology Methodology (BEM) Core on Thursday, September 4, 2025, from 2-3 pm ET for a session covering the fundamentals of causal inference. If you have ...

Recent statistics from the World Health Organization show that non-communicable diseases account for 74% of global fatalities, with lifestyle playing a pivotal role in their development. Promoting ...

During the peer-review process the editor and reviewers write an eLife assessment that summarises the significance of the findings reported in the article (on a scale ranging from landmark to useful) ...

Large Language Models (LLMs) have recently been used as experts to infer causal graphs, often by repeatedly applying a pairwise prompt that asks about the causal relationship of each variable pair.

aSchool of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia bClinical Epidemiology and Biostatistics Unit, Department of Paediatrics, University of Melbourne, ...