1994/06/01 by David Papineau · 2 citations
Mathematics · Computer Science · #Advanced Causal Inference Techniques #Statistical Methods and Inference #Bayesian Modeling and Causal Inference
paper · doi:10.1093/bjps/45.2.437
openalex publication_date 1994/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/02
Peter Urbach has argued, on Bayesian grounds, that experimental randomization serves no useful purpose in testing causal hypothesis. I maintain that he fails to distinguish general issues of statistical inference from specific problems involved in identifying causes. I concede the general Bayesian thesis that random sampling is inessential to sound statistical inference. But experimental randomization is a different matter, and often plays an essential role in our route to causal conclusions.