Abstract Generalizing empirical findings to new environments, settings, or populations is essential in most scientific explorations. This article treats a particular problem of generalizability, called “transportability”, defined as a license to transfer information learned in experimental...

Abstract Conventional wisdom dictates that the more we know about a problem domain the easier it is to predict the effects of policies in that domain. Strangely, this wisdom is not sanctioned by formal analysis, when the notions of “knowledge” and “policy” are given concrete definitions...

Abstract In this issue of the Causal, Casual, and Curious column, I compare several ways of extracting information from post-treatment variables and call attention to some peculiar relationships among them. In particular, I contrast do -calculus conditioning with counterfactual conditioning and...

Abstract This paper provides empirical interpretation of the do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus...