# Unsupervised Pairwise Causal Discovery on Heterogeneous Data using Mutual Information Measures

arXiv: https://arxiv.org/abs/2408.00399 · HTML: https://mlsanity.com/paper/2408.00399/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [Causal Discovery with Continuous Additive Noise Models](https://mlsanity.com/paper/1309.6779/) (2013)
- [Causal Discovery with Score Matching on Additive Models with Arbitrary Noise](https://mlsanity.com/paper/2304.03265/) (2023)
- [Measuring dependence powerfully and equitably](https://mlsanity.com/paper/1505.02213/) (2015)
- [Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow](https://mlsanity.com/paper/2212.01279/) (2022)

## Abstract (arXiv)

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.