I-FLOP: Fast Learning of Order and Parents from Interventional Data

2026-08-28

AI Summary

本文通过改进FLOP算法，使其适用于干预数据，并采用迭代Cholesky评分更新方法，有效解决了从干预数据中快速学习因果结构的问题。

Abstract

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.

Problem

• Interventional Data
• Order and Parents Learning
• Causal Structure

Innovation

• Interventional BIC Score
• Iterative Cholesky-based Score Updates
• Causal Structure Learning