Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect.
[10] The potential outcomes and regression analysis techniques handle such queries when data is collected using designed experiments.
Data collected in observational studies require different techniques for causal inference (because, for example, of issues such as confounding).
In academia, there are a significant number of theories on causality; The Oxford Handbook of Causation (Beebee, Hitchcock & Menzies 2009) encompasses 770 pages.
[13] To establish a correlation as causal within physics, it is normally understood that the cause and the effect must connect through a local mechanism (cf.
This, in turn, is challenged[dubious – discuss] by popular interpretations of the concepts of nonlinear systems and the butterfly effect, in which small events cause large effects due to, respectively, unpredictability and an unlikely triggering of large amounts of potential energy.
Because one cannot rewind history and replay events after making small controlled changes, causation can only be inferred, never exactly known.
[16] A major goal of scientific experiments and statistical methods is to approximate as best possible the counterfactual state of the world.
[18] Granger made the definition of probabilistic causality proposed by Norbert Wiener operational as a comparison of variances.
[19] Peter Spirtes, Clark Glymour, and Richard Scheines introduced the idea of explicitly not providing a definition of causality [clarification needed].