Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data

• URL: http://arxiv.org/abs/2505.08371v2
• Date: Fri, 16 May 2025 02:34:04 GMT
• Title: Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data
• Authors: Takashi Nicholas Maeda, Shohei Shimizu, Hidetoshi Matsui,
• Abstract summary: We introduce a novel approach that determines causal direction by analyzing the monotonicity of the conditional density ratio of the continuous variable.<n>Our theoretical analysis shows that the conditional density ratio exhibits monotonicity when the continuous variable causes the discrete variable, but not in the reverse direction.<n>This property provides a principled basis for comparing causal directions between variables of different types, free from strong distributional assumptions and bias arising from differences in their information content.
• Score: 5.142415132534398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a causal discovery method for mixed bivariate data consisting of one continuous and one discrete variable. Existing constraint-based approaches are ineffective in the bivariate setting, as they rely on conditional independence tests that are not suited to bivariate data. Score-based methods either impose strong distributional assumptions or face challenges in fairly comparing causal directions between variables of different types, due to differences in their information content. We introduce a novel approach that determines causal direction by analyzing the monotonicity of the conditional density ratio of the continuous variable, conditioned on different values of the discrete variable. Our theoretical analysis shows that the conditional density ratio exhibits monotonicity when the continuous variable causes the discrete variable, but not in the reverse direction. This property provides a principled basis for comparing causal directions between variables of different types, free from strong distributional assumptions and bias arising from differences in their information content. We demonstrate its effectiveness through experiments on both synthetic and real-world datasets, showing superior accuracy compared to existing methods.

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