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

arXiv: https://arxiv.org/abs/2608.28245 · HTML: https://mlsanity.com/paper/2608.28245/

## Key facts

- Cited by 1 papers 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 (most cited first, 8 of 8)

- [Causal inference using invariant prediction: identification and confidence intervals](https://mlsanity.com/paper/1501.01332/) (2015)
- [Permutation-based Causal Inference Algorithms with Interventions](https://mlsanity.com/paper/1705.10220/) (2017)
- [Permutation-Based Causal Structure Learning with Unknown Intervention Targets](https://mlsanity.com/paper/1910.09007/) (2019)
- [Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs](https://mlsanity.com/paper/1303.3216/) (2013)
- [Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions](https://mlsanity.com/paper/2211.14897/) (2022)
- [An Algorithm for the Construction of Bayesian Network Structures from Data](https://mlsanity.com/paper/1303.1485/) (2013)
- [Optimizing Causal Orderings for Generating DAGs from Data](https://mlsanity.com/paper/1303.5392/) (2013)
- [Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning](https://mlsanity.com/paper/2510.04970/) (2025)

## Built on by (most cited first, 1 of 1)

- [Score-Based Learning of Cluster DAGs from Interventions](https://mlsanity.com/paper/2610.11947/) (2026)

## 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.