Differentiable Constraint-Based Causal Discovery

• URL: http://arxiv.org/abs/2510.22031v1
• Date: Fri, 24 Oct 2025 21:28:39 GMT
• Title: Differentiable Constraint-Based Causal Discovery
• Authors: Jincheng Zhou, Mengbo Wang, Anqi He, Yumeng Zhou, Hessam Olya, Murat Kocaoglu, Bruno Ribeiro,
• Abstract summary: Causal discovery from observational data is a fundamental task in artificial intelligence.<n>Existing methods can be broadly categorized as constraint-based or score-based approaches.<n>This work explores developing differentiable $d$-separation scores, obtained through a percolation theory using soft logic.
• Score: 18.720260801912346
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Causal discovery from observational data is a fundamental task in artificial intelligence, with far-reaching implications for decision-making, predictions, and interventions. Despite significant advances, existing methods can be broadly categorized as constraint-based or score-based approaches. Constraint-based methods offer rigorous causal discovery but are often hindered by small sample sizes, while score-based methods provide flexible optimization but typically forgo explicit conditional independence testing. This work explores a third avenue: developing differentiable $d$-separation scores, obtained through a percolation theory using soft logic. This enables the implementation of a new type of causal discovery method: gradient-based optimization of conditional independence constraints. Empirical evaluations demonstrate the robust performance of our approach in low-sample regimes, surpassing traditional constraint-based and score-based baselines on a real-world dataset. Code and data of the proposed method are publicly available at https://github$.$com/PurdueMINDS/DAGPA.

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