mlsanityData to 14 Oct 2026

Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning

arXiv 2510.04970 ↗First posted 6 Oct 2025stat.ML cs.AI cs.LGcited by 4

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Abstract (arXiv)

We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score updates, cutting run-times over prior algorithms. This makes it feasible to fully embrace discrete search, enabling iterated local search with principled order initialization to find graphs with scores at or close to the global optimum. The resulting structures are highly accurate across benchmarks, with near-perfect recovery in standard settings. This performance calls for revisiting discrete search over graphs as a reasonable approach to causal discovery.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 24 of its 58 references that are in the map. First posted on arXiv 6 Oct 2025.

Builds on

24 of 58 references.

References · 24

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
  3. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 187
  4. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
  5. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 140
  6. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 111
  7. 2013Causal Networks: Semantics and Expressivenesscited by 67
  8. 2022Greedy Relaxations of the Sparsest Permutation Algorithmcited by 64
  9. 2013Learning Sparse Causal Models is not NP-hardcited by 49
  10. 2015High-dimensional consistency in score-based and hybrid structure learningcited by 46
  11. 2023Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Treescited by 38
  12. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 36
12 more
  1. 2018Efficient Sampling and Structure Learning of Bayesian Networkscited by 28
  2. 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Modelscited by 28
  3. 2023textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discoverycited by 18
  4. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 18
  5. 2024Adjustment Identification Distance: A gadjid for Causal Structure Learningcited by 16
  6. 2015Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluationscited by 14
  7. 2021Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithmscited by 11
  8. 2025Extremely Greedy Equivalence Searchcited by 8
  9. 2023Causal discovery in a complex industrial system: A time series benchmarkcited by 7
  10. 2024Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Methodcited by 6
  11. 2025What is causal about causal models and representations?cited by 6
  12. 2013MIDAS - An Influence Diagram for Management of Mildew in Winter Wheatcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026I-FLOP: Fast Learning of Order and Parents from Interventional Datacited by 1
  2. 2026Learning Causal Structure of Time Series using Best Order Score Searchcited by 0
  3. 2026A Topological Sorting Criterion for Random Causal Directed Acyclic Graphscited by 0
  4. 2026Score-Based Learning of Cluster DAGs from Interventionscited by 0