Rubin causal model
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, 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.
Introduction
Causal model
A causal model is an abstract model that describes the causal mechanisms of a system. The model must express more than correlation because correlation does not imply causation.
Judea Pearl defines a causal model as an ordered triple 
, 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.
Causal diagram
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.
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