mlsanityData to 7 Oct 2026

Permutation-based Causal Inference Algorithms with Interventions

arXiv 1705.10220 ↗First posted 29 May 2017stat.ME stat.APcited by 78

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Abstract (arXiv)

Learning directed acyclic graphs using both observational and interventional data is now a fundamentally important problem due to recent technological developments in genomics that generate such single-cell gene expression data at a very large scale. In order to utilize this data for learning gene regulatory networks, efficient and reliable causal inference algorithms are needed that can make use of both observational and interventional data. In this paper, we present two algorithms of this type and prove that both are consistent under the faithfulness assumption. These algorithms are interventional adaptations of the Greedy SP algorithm and are the first algorithms using both observational and interventional data with consistency guarantees. Moreover, these algorithms have the advantage that they are nonparametric, which makes them useful also for analyzing non-Gaussian data. In this paper, we present these two algorithms and their consistency guarantees, and we analyze their performance on simulated data, protein signaling data, and single-cell gene expression data.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 May 2017.

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Citing papers per year: 2021: 7; 2022: 16; 2023: 11; 2024: 15; 2025: 15; 2026: 14.

78 papers.

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  1. 2022Amortized Inference for Causal Structure Learningcited by 89
  2. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  3. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  4. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  5. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  6. 2022Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modelingcited by 33
  7. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  8. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  9. 2022Differentiable Causal Discovery Under Latent Interventionscited by 21
  10. 2023NODAGS-Flow: Nonlinear Cyclic Causal Structure Learningcited by 20
  11. 2023Causal Modeling with Stationary Diffusionscited by 19
  12. 2024Sample, estimate, aggregate: A recipe for causal discovery foundation modelscited by 19
38 more
  1. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  2. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  3. 2022Causal Entropy Optimizationcited by 13
  4. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  5. 2023Subset verification and search algorithms for causal DAGscited by 12
  6. 2021Benchpress: A Scalable and Versatile Workflow for Benchmarking Structure Learning Algorithmscited by 11
  7. 2022A Meta-Reinforcement Learning Algorithm for Causal Discoverycited by 11
  8. 2024Gene Regulatory Network Inference in the Presence of Dropouts: a Causal Viewcited by 11
  9. 2025Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learningcited by 11
  10. 2021Scalable Intervention Target Estimation in Linear Modelscited by 10
  11. 2023Active causal structure learning with advicecited by 10
  12. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  13. 2022Causal inference in drug discovery and developmentcited by 6
  14. 2024Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approachcited by 6
  15. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 6
  16. 2021Variance Minimization in the Wasserstein Space for Invariant Causal Predictioncited by 5
  17. 2021A Fast Non-parametric Approach for Local Causal Structure Learningcited by 5
  18. 2022The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionscited by 4
  19. 2024On Discovery of Local Independence over Continuous Variables via Neural Contextual Decompositioncited by 4
  20. 2024Causal Inference with Latent Variables: Recent Advances and Future Prospectivescited by 4
  21. 2024Generative Intervention Models for Causal Perturbation Modelingcited by 4
  22. 2025Differentiable Cyclic Causal Discovery Under Unmeasured Confounderscited by 4
  23. 2025Does TabPFN Understand Causal Structures?cited by 4
  24. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
  25. 2022Sequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scorescited by 3
  26. 2022Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networkscited by 3
  27. 2023dotears: Scalable, consistent DAG estimation using observational and interventional datacited by 3
  28. 2023Causal Effect Estimation from Observational and Interventional Data Through Matrix Weighted Linear Estimatorscited by 3
  29. 2023Differentiable Bayesian Structure Learning with Acyclicity Assurancecited by 3
  30. 2025No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biologycited by 3
  31. 2025Causal Structure and Representation Learning with Biomedical Applicationscited by 3
  32. 2023Bayesian causal discovery from unknown general interventionscited by 2
  33. 2024Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experimentscited by 2
  34. 2025Characterization and Learning of Causal Graphs from Hard Interventionscited by 2
  35. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2
  36. 2025Causal Structure Learning for Dynamical Systems with Theoretical Score Analysiscited by 2
  37. 2026Coarsening Causal DAG Modelscited by 2
  38. 2026Efficient Learning of Stationary Diffusions with Stein-type Discrepanciescited by 2

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