{"ID":2850862,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22031","arxiv_id":"2510.22031","title":"Differentiable Constraint-Based Causal Discovery","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.22031","url_pdf":"https://arxiv.org/pdf/2510.22031v2","authors":"[\"Jincheng Zhou\",\"Mengbo Wang\",\"Anqi He\",\"Yumeng Zhou\",\"Hessam Olya\",\"Murat Kocaoglu\",\"Bruno Ribeiro\"]","published":"2025-10-24T21:28:39Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"stat.ML\"]","methods":"[]","project_urls":"[\"https://github$.$com/PurdueMINDS/DAGPA\"]","has_code":false}