I-FLOP：从干预数据中快速学习顺序与父节点的算法
I-FLOP: Fast Learning of Order and Parents from Interventional Data
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中文总结 AI 辅助
该研究将FLOP算法扩展至干预数据，提出I-FLOP，其可恢复符合干预马尔可夫等价类的DAG，在真实与模拟干预数据上的因果结构学习中，性能和运行时间均优于现有算法。
中文摘要 AI 辅助
我们将Wienöbst等人（2026）近期提出的FLOP（快速学习顺序与父节点）算法从观测数据扩展至干预数据。具体而言，我们使用Hauser和Bühlmann（2012）的干预BIC分数，并对其进行调整，使其可与基于Cholesky的迭代分数更新结合使用，而该更新是FLOP算法速度的部分原因。我们证明，在样本极限下，I-FLOP可恢复出与数据生成DAG属于同一干预马尔可夫等价类的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，结果显示I-FLOP在性能和运行时间两方面均表现更优。
英文摘要
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.
