Neyman-Rubin Causal Model
Jump to navigation
Jump to search
A Neyman-Rubin Causal Model is a causality analysis method that is based on counterfactual conditional outcomes.
- See: Jerzy Neyman, Statistical Analysis, Causality, Conceptual Framework, Counterfactual Conditional, Donald Rubin, Paul W. Holland, Journal of The American Statistical Association.
References
2017
- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Rubin_causal_model Retrieved:2017-11-10.
- The Rubin causal model (RCM), also known as the Neyman–Rubin causal model, is an approach to the statistical analysis of cause and effect based on the framework of potential outcomes, named after Donald Rubin. The name "Rubin causal model" was first coined by Rubin's graduate school colleague, Paul W. Holland. The potential outcomes framework was first proposed by Jerzy Neyman in his 1923 Master's thesis,[1] though he discussed it only in the context of completely randomized experiments. Rubin, together with other contemporary statisticians, extended it into a general framework for thinking about causation in both observational and experimental studies.
- ↑ Neyman, Jerzy. Sur les applications de la theorie des probabilites aux experiences agricoles: Essai des principes. Master's Thesis (1923). Excerpts reprinted in English, Statistical Science, Vol. 5, pp. 463–472. (D. M. Dabrowska, and T. P. Speed, Translators.)