Many aspects of statistical design, modelling, and inference have close and important connections with causal thinking. These are analyzed in the paper against a philosophical background that regards formal mathematical models as having dual interpretations, reflecting both objectivist reality and subjectivist rationality. The latter aspect weakens the need for an objective theory of probabilistic causation, and suggests that a traditional image of causes as deterministic mechanisms should remain primary. It is argued that such causes should guide much preformal thinking about what to include in formal statistical models, especially of dynamic phenomena. The statistical measurement of causal effects is facilitated by good statistical design, including randomization where feasible, and requires other methodologies for controlling and assessing uncertainties, for example in model construction and inference. Illustrative examples include case studies where the problem is to assess retrospectively the causes of observed events and where the task is to assess future risks from controllable factors.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education