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Causal Discovery from Changes

arXiv 1301.2312 ↗First posted 10 Jan 2013cs.AIcited by 52

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Abstract (arXiv)

We propose a new method of discovering causal structures, based on the detection of local, spontaneous changes in the underlying data-generating model. We analyze the classes of structures that are equivalent relative to a stream of distributions produced by local changes, and devise algorithms that output graphical representations of these equivalence classes. We present experimental results, using simulated data, and examine the errors associated with detection of changes and recovery of structures.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jan 2013.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 10; 2023: 10; 2024: 11; 2025: 7; 2026: 7.

52 papers.

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  1. 2021Towards Causal Representation Learningcited by 207
  2. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 105
  3. 2022Amortized Inference for Causal Structure Learningcited by 89
  4. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  5. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited 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. 2021Agent Incentives: A Causal Perspectivecited by 51
  9. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  10. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  11. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  12. 2022From Statistical to Causal Learningcited by 40
38 more
  1. 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
  2. 2021An Online Learning Approach to Interpolation and Extrapolation in Domain Generalizationcited by 30
  3. 2024Root Cause Analysis of Outliers with Missing Structural Knowledgecited by 19
  4. 2023Causal Policy Gradient for Whole-Body Mobile Manipulationcited by 18
  5. 2021FedDAG: Federated DAG Structure Learningcited by 17
  6. 2023Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounderscited by 17
  7. 2021Optimization-based Causal Estimation from Heterogenous Environmentscited by 16
  8. 2022Variational Causal Dynamics: Discovering Modular World Models from Interventionscited by 16
  9. 2022Core Risk Minimization using Salient ImageNetcited by 15
  10. 2024RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Modelcited by 13
  11. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  12. 2025Towards Interpretable Deep Generative Models via Causal Representation Learningcited by 12
  13. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  14. 2021Neural Networks for Learning Counterfactual G-Invariances from Single Environmentscited by 8
  15. 2022Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesiscited by 8
  16. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 6
  17. 2024Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithmcited by 5
  18. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
  19. 2023Estimating Treatment Effects in Continuous Time with Hidden Confounderscited by 3
  20. 2023Model-based causal feature selection for general response typescited by 3
  21. 2025Causal Structure and Representation Learning with Biomedical Applicationscited by 3
  22. 2022Causal Structure Learning with Recommendation Systemcited by 2
  23. 2023Bayesian causal discovery from unknown general interventionscited by 2
  24. 2024Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Modelscited by 2
  25. 2024Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methodscited by 2
  26. 2024Estimating Joint Interventional Distributions from Marginal Interventional Datacited by 2
  27. 2024Cross-validating causal discovery via Leave-One-Variable-Outcited by 2
  28. 2025Characterization and Learning of Causal Graphs from Hard Interventionscited by 2
  29. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2
  30. 2026Test-Time Learning of Causal Structure from Interventional Datacited by 2
  31. 2024Identifying biological perturbation targets through causal differential networkscited by 1
  32. 2024Causal reasoning in difference graphscited by 1
  33. 2025MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventionscited by 1
  34. 2024Continual Learning of Nonlinear Independent Representationscited by 0
  35. 2024Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testingcited by 0
  36. 2026Teleological Inference in Structural Causal Models via Intentional Interventionscited by 0
  37. 2026Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretrainingcited by 0
  38. 2026Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Modelscited by 0

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