Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures

TL;DR

This paper introduces an unsupervised method for pairwise causal discovery using mutual information measures, emphasizing robustness and generalizability across different variable types, and providing unbiased standard results for unknown environments.

Contribution

It proposes a novel unsupervised approach for causal discovery with mutual information, challenging supervised baselines and focusing on heterogeneous data.

Findings

Unsupervised mutual information-based causal discovery outperforms supervised methods.

The approach is robust across different variable types.

Provides standard unbiased results for unknown environments.

Abstract

A fundamental task in science is to determine the underlying causal relations because it is the knowledge of this functional structure what leads to the correct interpretation of an effect given the apparent associations in the observed data. In this sense, Causal Discovery is a technique that tackles this challenge by analyzing the statistical properties of the constituent variables. In this work, we target the generalizability of the discovery method by following a reductionist approach that only involves two variables, i.e., the pairwise or bi-variate setting. We question the current (possibly misleading) baseline results on the basis that they were obtained through supervised learning, which is arguably contrary to this genuinely exploratory endeavor. In consequence, we approach this problem in an unsupervised way, using robust Mutual Information measures, and observing the impact of the different variable types, which is oftentimes ignored in the design of solutions. Thus, we provide a novel set of standard unbiased results that can serve as a reference to guide future discovery tasks in completely unknown environments.