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Judea Pearl defines a causal model as an ordered triple <math>\langle U, V, E\rangle</math>, where U is a set of exogenous variables whose values are determined by factors outside the model; V is a set of endogenous variables whose values are determined by factors within the model; and E is a set of structural equations that express the value of each endogenous variable as a function of the values of the other variables in U and V.
A causal diagram is a graphical tool that enables the visualisation of causal relationships between variables in a causal model. A typical causal diagram will comprise a set of variables (or nodes) defined as being within the scope of the model being represented. Any variable in the diagram should be connected by an arrow to another variable with which it has a causal influence - the arrowhead delineates the direction of this causal relationship, e.g., an arrow connecting variables A and B with the arrowhead at B indicates a relationship whereby (all other factors being equal) a qualitative or quantitative change in A may cause change in B.
- Causal network – a Bayesian network with an explicit requirement that the relationships be causal
- Structural equation modeling – a statistical technique for testing and estimating causal relations
- Path analysis (statistics)
- Pearl, Judea (2000). Causality: Models, Reasoning, and Inference, Cambridge University Press.
- Greenland, S.; Brumback, B. (2002). "An overview of relations among causal modelling methods". International Journal of Epidemiology 31 (5): 1030–1037. PMID 12435780. doi:10.1093/ije/31.5.1030.
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