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Permutation-Based Causal Structure Learning with Unknown Intervention Targets

arXiv 1910.09007 ↗First posted 20 Oct 2019stat.MEcited by 69

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Abstract (arXiv)

We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention targets are partially or completely unknown. This problem is highly relevant for example in genomics, since gene knockout technologies are known to have off-target effects. We characterize the interventional Markov equivalence class of DAGs that can be identified from interventional data with unknown intervention targets. In addition, we propose a provably consistent algorithm for learning the interventional Markov equivalence class from such data. The proposed algorithm greedily searches over the space of permutations to minimize a novel score function. The algorithm is nonparametric, which is particularly important for applications to genomics, where the relationships between variables are often non-linear and the distribution non-Gaussian. We demonstrate the performance of our algorithm on synthetic and biological datasets. Links to an implementation of our algorithm and to a reproducible code base for our experiments can be found at https://uhlerlab.github.io/causaldag/utigsp.

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2019.

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Citing papers per year: 2021: 5; 2022: 13; 2023: 14; 2024: 13; 2025: 12; 2026: 12.

69 papers.

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  1. 2021Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICAcited by 158
  2. 2023Learning Linear Causal Representations from Interventions under General Nonlinear Mixingcited by 92
  3. 2022Linear Causal Disentanglement via Interventionscited by 84
  4. 2022Learning to Induce Causal Structurecited by 68
  5. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  6. 2022Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesiscited by 54
  7. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  8. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  9. 2023BISCUIT: Causal Representation Learning from Binary Interactionscited by 38
  10. 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
  11. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  12. 2024Counterfactual Image Editingcited by 20
38 more
  1. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  2. 2023Optimizing NOTEARS Objectives via Topological Swapscited by 17
  3. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  4. 2021Matching a Desired Causal State via Shift Interventionscited by 16
  5. 2024Linear Causal Representation Learning from Unknown Multi-node Interventionscited by 14
  6. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  7. 2023CoLiDE: Concomitant Linear DAG Estimationcited by 12
  8. 2024The Causal Chambers: Real Physical Systems as a Testbed for AI Methodologycited by 12
  9. 2025Towards Interpretable Deep Generative Models via Causal Representation Learningcited by 12
  10. 2021Scalable Intervention Target Estimation in Linear Modelscited by 10
  11. 2022Valid Inference After Causal Discoverycited by 10
  12. 2023DAG Learning on the Permutahedroncited by 9
  13. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  14. 2023Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Datacited by 7
  15. 2022Disentangling Mixtures of Unknown Causal Interventionscited by 6
  16. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 6
  17. 2021Variance Minimization in the Wasserstein Space for Invariant Causal Predictioncited by 5
  18. 2022Invariant Ancestry Searchcited by 5
  19. 2023iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Modelscited by 5
  20. 2024Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithmcited by 5
  21. 2022The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionscited by 4
  22. 2024Generative Intervention Models for Causal Perturbation Modelingcited by 4
  23. 2025Multi-Domain Causal Discovery in Bijective Causal Modelscited by 4
  24. 2025Curious Causality-Seeking Agents Learn Meta Causal Worldcited by 4
  25. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
  26. 2026Causal Representation Learning from General Environments under Nonparametric Mixingcited by 4
  27. 2023dotears: Scalable, consistent DAG estimation using observational and interventional datacited by 3
  28. 2023Differentiable Bayesian Structure Learning with Acyclicity Assurancecited by 3
  29. 2024Interventional Causal Discovery in a Mixture of DAGscited by 3
  30. 2024CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Seriescited by 3
  31. 2025Revealing Multimodal Causality with Large Language Modelscited by 3
  32. 2025Causal Structure and Representation Learning with Biomedical Applicationscited by 3
  33. 2022Causal Structure Learning with Recommendation Systemcited by 2
  34. 2023Discovering Optimal Scoring Mechanisms in Causal Strategic Predictioncited by 2
  35. 2023Bayesian causal discovery from unknown general interventionscited by 2
  36. 2024Causal Discovery under Off-Target Interventionscited by 2
  37. 2025Causal Effect Identification in Heterogeneous Environments from Higher-Order Momentscited by 2
  38. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2

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