基于干预数据快速学习因果图结构，速度与精度均优于现有方法。
I-FLOP: Fast Learning of Order and Parents from Interventional Data
- 利用干预BIC评分结合迭代乔列斯基更新，加速因果结构推断。
- 在大样本下能准确恢复与真实因果图同属一个干预等价类的图结构。
- 适合需要高效处理干预实验数据的科研人员或工业应用。
我们扩展了Wienöbst等人（2026）提出的FLOP算法，使其从观测数据推广到干预数据。具体而言，采用Hauser和Bühlmann（2012）提出的干预BIC评分，并将其适配至部分推动FLOP高速性的迭代乔列斯基得分更新机制中。我们证明，在样本极限下，I-FLOP可恢复出与生成数据的真实因果图处于同一干预马尔可夫等价类的有向无环图（DAG）。我们在真实和模拟的干预数据上将I-FLOP与现有因果结构学习算法进行比较，结果表明其在性能和运行时间方面均表现优异。
原文摘要 · Abstract (English)
We extend the FLOP (fast learning of order and parents) algorithm recently proposed by Wienöbst et al. (2026) from observational to interventional data. In particular, we use the interventional BIC score of Hauser and Bühlmann (2012), adapting it to be used with the iterative Cholesky-based score updates that are partly responsible for FLOP's speed. We show that, in the sample limit, I-FLOP recovers a DAG in the same interventional Markov equivalence class as the data-generating DAG. We compare I-FLOP to existing causal structure learning algorithms on real and simulated interventional data, where it performs favorably in terms of both performance and run time.
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