用整数规划设计最少干预实验，精准识别因果关系。
Causal Discovery by Interventions via Integer Programming
- 基于整数规划构建最小干预集，确保因果结构可识别。
- 在多种实验条件下验证，能高效找到最优干预方案。
- 适合需要精确控制变量的科研场景，如生物医学研究。
因果发现对多个科学领域至关重要，用于揭示数据中的因果结构。传统依赖观测数据的方法受限于混杂变量的影响。本文提出一种基于优化的整数规划（IP）方法，用于设计最小干预集，以确保因果结构的可识别性。该方法提供精确且模块化的解决方案，可适应不同的实验设置与约束条件。通过多组对比分析，验证了其有效性、适用性和鲁棒性。
原文摘要 · Abstract (English)
Causal discovery is essential across various scientific fields to uncover causal structures within data. Traditional methods relying on observational data have limitations due to confounding variables. This paper presents an optimization-based approach using integer programming (IP) to design minimal intervention sets that ensure causal structure identifiability. Our method provides exact and modular solutions that can be adjusted to different experimental settings and constraints. We demonstrate its effectiveness through comparative analysis across different settings, demonstrating its applicability and robustness.
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