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.

本研究解决了因果发现领域中基于监督学习方法可能导致的误导性结果问题，提出了一种无监督的二变量因果发现方法。通过使用稳健的互信息度量，考虑了不同变量类型的影响，提供了一组标准的无偏结果，以指导未来在完全未知环境下的因果发现任务。

基于互信息度量的异构数据无监督对因果发现