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[2602.15306] Sparse Additive Model Pruning for Order-Based Causal Structure Learning
Summary
This paper presents a novel pruning method for causal structure learning using sparse additive models, improving computational efficiency and estimation accuracy over existing techniques.
Why It Matters
Causal structure learning is essential for understanding relationships in data. This study addresses the computational challenges of pruning methods, offering a faster and potentially more accurate alternative that can benefit researchers and practitioners in machine learning and statistics.
Key Takeaways
- Introduces a new pruning method based on sparse additive models.
- Addresses computational bottlenecks of existing CAM-pruning techniques.
- Demonstrates superior speed and comparable accuracy in experiments.
- Combines randomized tree embedding with group-wise sparse regression.
- Enhances efficiency in causal structure learning from observational data.
Statistics > Machine Learning arXiv:2602.15306 (stat) [Submitted on 17 Feb 2026] Title:Sparse Additive Model Pruning for Order-Based Causal Structure Learning Authors:Kentaro Kanamori, Hirofumi Suzuki, Takuya Takagi View a PDF of the paper titled Sparse Additive Model Pruning for Order-Based Causal Structure Learning, by Kentaro Kanamori and 2 other authors View PDF HTML (experimental) Abstract:Causal structure learning, also known as causal discovery, aims to estimate causal relationships between variables as a form of a causal directed acyclic graph (DAG) from observational data. One of the major frameworks is the order-based approach that first estimates a topological order of the underlying DAG and then prunes spurious edges from the fully-connected DAG induced by the estimated topological order. Previous studies often focus on the former ordering step because it can dramatically reduce the search space of DAGs. In practice, the latter pruning step is equally crucial for ensuring both computational efficiency and estimation accuracy. Most existing methods employ a pruning technique based on generalized additive models and hypothesis testing, commonly known as CAM-pruning. However, this approach can be a computational bottleneck as it requires repeatedly fitting additive models for all variables. Furthermore, it may harm estimation quality due to multiple testing. To address these issues, we introduce a new pruning method based on sparse additive models, which enables d...