{"ID":2864411,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23992","arxiv_id":"2509.23992","title":"Guide: Generalized-Prior and Data Encoders for DAG Estimation","abstract":"Modern causal discovery methods face critical limitations in scalability, computational efficiency, and adaptability to mixed data types, as evidenced by benchmarks on node scalability (30, $\\le 50$, $\\ge 70$ nodes), computational energy demands, and continuous/non-continuous data handling. While traditional algorithms like PC, GES, and ICA-LiNGAM struggle with these challenges, exhibiting prohibitive energy costs for higher-order nodes and poor scalability beyond 70 nodes, we propose \\textbf{GUIDE}, a framework that integrates Large Language Model (LLM)-generated adjacency matrices with observational data through a dual-encoder architecture. GUIDE uniquely optimizes computational efficiency, reducing runtime on average by $\\approx 42%$ compared to RL-BIC and KCRL methods, while achieving an average $\\approx 117%$ improvement in accuracy over both NOTEARS and GraN-DAG individually. During training, GUIDE's reinforcement learning agent dynamically balances reward maximization (accuracy) and penalty avoidance (DAG constraints), enabling robust performance across mixed data types and scalability to $\\ge 70$ nodes -- a setting where baseline methods fail.","short_abstract":"Modern causal discovery methods face critical limitations in scalability, computational efficiency, and adaptability to mixed data types, as evidenced by benchmarks on node scalability (30, $\\le 50$, $\\ge 70$ nodes), computational energy demands, and continuous/non-continuous data handling. While traditional algorithms...","url_abs":"https://arxiv.org/abs/2509.23992","url_pdf":"https://arxiv.org/pdf/2509.23992v1","authors":"[\"Amartya Roy\",\"Devharish N\",\"Shreya Ganguly\",\"Kripabandhu Ghosh\"]","published":"2025-09-28T17:35:21Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"Large Language Model\",\"Language Model\"]","has_code":false}