From dependency to causality: a machine learning approach

TL;DR

This paper introduces a supervised machine learning method that leverages asymmetries in conditional dependence relations to infer causal links in multivariate data, extending causal inference capabilities beyond pairwise analysis.

Contribution

It presents a novel approach using supervised learning to detect causal relationships in multivariate settings based on asymmetries in conditional dependence, advancing causal inference methods.

Findings

Supervised learning can effectively identify causal links in multivariate data.

Asymmetries in conditional dependence relations are useful causal indicators.

Method extends causal inference to more complex, multivariate distributions.

Abstract

The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference. Recent results in the ChaLearn cause-effect pair challenge have shown that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations thanks to data driven approaches. This paper proposes a supervised machine learning approach to infer the existence of a directed causal link between two variables in multivariate settings with $n>2$ variables. The approach relies on the asymmetry of some conditional (in)dependence relations between the members of the Markov blankets of two variables causally connected. Our results show that supervised learning methods may be successfully used to extract causal information on the basis of asymmetric statistical descriptors also for $n>2$ variate distributions.