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Workshop: Causal Machine Learning for Real-World Impact

Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow

Felipe Giori · Flavio Figueiredo


Pairwise Causal Discovery is the task of determining causal, anti-causal, confounded or independence relationships from real-world datasets (i.e., pairs of variables). Over the last few years, this challenging task has subsidized not only the discovery of novel machine learning models aimed at solving the task, but also discussions on how learning the causal direction of variables may benefit machine learning overall. In this paper, we show that Quantitative Information Flow (QIF), a measure usually employed for measuring leakages of information from a system to an attacker, shows promising results as features for the causal discovery task. In particular, experiments with real-world datasets indicate that QIF is statistically tied to the state of the art. Our initial results motivate further inquiries on how QIF relates to causality and what are its limitations.

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