I-FLOP: Fast Learning of Order and Parents from Interventional Data
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Abstract (arXiv)
We extend the FLOP (fast learning of order and parents) algorithm recently proposed by Wien"obst et al. (2026) from observational to interventional data. In particular, we use the interventional BIC score of Hauser and B"uhlmann (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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 28 Aug 2026.
Builds on
8 of 22 references.
References · 8
- 2015Causal inference using invariant prediction: identification and confidence intervalscited by 536
- 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
- 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
- 2013Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphscited by 25
- 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
- 2013An Algorithm for the Construction of Bayesian Network Structures from Datacited by 5
- 2013Optimizing Causal Orderings for Generating DAGs from Datacited by 5
- 2025Embracing Discrete Search: A Reasonable Approach to Causal Structure Learningcited by 4
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Citing papers per year: 2026: 1.
1 paper.
- 2026Score-Based Learning of Cluster DAGs from Interventionscited by 0
- 2026Score-Based Learning of Cluster DAGs from Interventions8 Oct 2026