[12] 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).
[16] Granger made the definition of probabilistic causality proposed by Norbert Wiener operational as a comparison of variances.
[5][18] Peter Spirtes, Clark Glymour, and Richard Scheines introduced the idea of explicitly not providing a definition of causality.
ECA is used in physics to understand the physical causal mechanisms of the system, e.g., in geophysics using the PC-stable algorithm (a variant of the original PC algorithm)[30] and in dynamical systems using pairwise asymmetric inference (a variant of convergent cross mapping).
[7] Response to the criticism points out that assumptions used for developing ECA techniques may not hold for a given data set[3][14][32][33][34] and that any causal relationships discovered during ECA are contingent on these assumptions holding true[25][35] There is also a collection of tools and data maintained by the Causality Workbench team [12] and the CCD team [13].