From dependency to causality: a machine learning approach

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference and can be summarized by two famous statements: correlation (or more generally statistical association) does not imply causation and causation induces a statistical dependency between causes and effects (or more generally descendants) ([26]). In other terms it is well known that statistical dependency is a necessary yet not sufficient condition for causality. The unidirectional link between these 1 two notions has been used by many formal approaches to causality to justify the adoption of statistical methods for detecting or inferring causal links from observational data. The most influential one is the Causal Bayesian Network approach, detailed in ([17]) which relies on notions of independence and conditional independence to detect causal patterns in the data. Well known examples of related inference algorithms are the constraint-based methods like the PC algorithms ([30]) and IC ([23]). These approaches are founded on probability theory and have been shown to be accurate in reconstructing causal patterns in many applications.