mlsanityData to 7 Oct 2026

Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions

arXiv 1802.06310 ↗First posted 17 Feb 2018stat.ME math.ST stat.APcited by 76

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Abstract (arXiv)

We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available. This setting is common in biology, where gene regulatory networks can be intervened on using chemical reagents or gene deletions. Hauser and B"uhlmann (2012) previously characterized the identifiability of causal DAGs under perfect interventions, which eliminate dependencies between targeted variables and their direct causes. In this paper, we extend these identifiability results to general interventions, which may modify the dependencies between targeted variables and their causes without eliminating them. We define and characterize the interventional Markov equivalence class that can be identified from general (not necessarily perfect) intervention experiments. We also propose the first provably consistent algorithm for learning DAGs in this setting and evaluate our algorithm on simulated and biological datasets.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2018.

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Citing papers per year: 2021: 5; 2022: 14; 2023: 14; 2024: 10; 2025: 16; 2026: 17.

76 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2022Interventional Causal Representation Learningcited by 140
  3. 2022Linear Causal Disentanglement via Interventionscited by 84
  4. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  5. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  6. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  7. 2022Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesiscited by 54
  8. 2023Score-based Causal Representation Learning with Interventionscited by 49
  9. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  10. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  11. 2021Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspectivecited by 40
  12. 2022Active Bayesian Causal Inferencecited by 34
38 more
  1. 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
  2. 2022Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoningcited by 33
  3. 2023Stable Differentiable Causal Discoverycited by 30
  4. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  5. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  6. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  7. 2021Matching a Desired Causal State via Shift Interventionscited by 16
  8. 2022Causal Entropy Optimizationcited by 13
  9. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  10. 2022A Meta-Reinforcement Learning Algorithm for Causal Discoverycited by 11
  11. 2024Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learningcited by 10
  12. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  13. 2023The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation datacited by 7
  14. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 6
  15. 2023iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Modelscited by 5
  16. 2024Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithmcited by 5
  17. 2023Competence-Based Analysis of Language Modelscited by 4
  18. 2024Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Datacited by 4
  19. 2024Generative Intervention Models for Causal Perturbation Modelingcited by 4
  20. 2025Multi-Domain Causal Discovery in Bijective Causal Modelscited by 4
  21. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
  22. 2026Causal Representation Learning from General Environments under Nonparametric Mixingcited by 4
  23. 2022Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networkscited by 3
  24. 2023dotears: Scalable, consistent DAG estimation using observational and interventional datacited by 3
  25. 2023Meek Separators and Their Applications in Targeted Causal Discoverycited by 3
  26. 2024Interventional Causal Discovery in a Mixture of DAGscited by 3
  27. 2024BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learningcited by 3
  28. 2024CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Seriescited by 3
  29. 2025No Foundations without Foundations -- Why semi-mechanistic models are essential for regulatory biologycited by 3
  30. 2025Revealing Multimodal Causality with Large Language Modelscited by 3
  31. 2025Causal Structure and Representation Learning with Biomedical Applicationscited by 3
  32. 2026CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpadcited by 3
  33. 2023Discovering Optimal Scoring Mechanisms in Causal Strategic Predictioncited by 2
  34. 2023Bayesian causal discovery from unknown general interventionscited by 2
  35. 2025Characterization and Learning of Causal Graphs from Hard Interventionscited by 2
  36. 2025Regret-Based Federated Causal Discovery with Unknown Interventionscited by 2
  37. 2026Latent Causal Diffusions for Single-Cell Perturbation Modelingcited by 2
  38. 2026Test-Time Learning of Causal Structure from Interventional Datacited by 2

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