Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four...

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main...

analysis is a form of inductive (first create a theory [root] based on empirical evidence [causes]) and deductive (test the theory [underlying causal...

Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. Exploratory causal analysis...

Causal Layered Analysis (CLA) is a future research theory that integrates various epistemic modes, creates spaces for alternative futures, and consists...

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...

causal modeling and analysis of covariance structures. Path analysis is considered by Judea Pearl to be a direct ancestor to the techniques of causal...

graphical models to non-parametric analysis, and thus achieved a generality and flexibility that has transformed causal analysis in computer science, epidemiology...

Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation...

contributing cause" (but not necessarily the only contributing cause). Causal analysis is the field of experimental design and statistics pertaining to establishing...