用智能实验设计减少因果图推断所需实验次数
From Observation to Orientation: an Adaptive Integer Programming Approach to Intervention Design
- 基于因果图与预算约束，迭代优化干预选择
- 实验次数和变量操作数均显著低于随机方法
- 适用于有实际资源限制的因果推断场景
结合观测数据与实验数据，本研究提出一种自适应干预设计范式，可在实际预算约束下高效恢复因果有向无环图（DAG）。为在约束条件下最大化信息增益，提出一种迭代整数规划（IP）方法，大幅减少所需实验次数。通过大规模模拟验证，该方法在不同图规模与边密度下均能以更少的干预轮次和变量操作完成完整因果图恢复，且具备良好灵活性，可适配多种现实约束。
原文摘要 · Abstract (English)
Using both observational and experimental data, a causal discovery process can identify the causal relationships between variables. A unique adaptive intervention design paradigm is presented in this work, where causal directed acyclic graphs (DAGs) are for effectively recovered with practical budgetary considerations. In order to choose treatments that optimize information gain under these considerations, an iterative integer programming (IP) approach is proposed, which drastically reduces the number of experiments required. Simulations over a broad range of graph sizes and edge densities are used to assess the effectiveness of the suggested approach. Results show that the proposed adaptive IP approach achieves full causal graph recovery with fewer intervention iterations and variable manipulations than random intervention baselines, and it is also flexible enough to accommodate a variety of practical constraints.
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