mlsanityData to 13 Oct 2026

Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

arXiv 2001.11940 ↗First posted 31 Jan 2020stat.ML cs.LGcited by 24

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Abstract (arXiv)

We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG). We provide a graphical representation of such mixture distributions and prove that this representation encodes the conditional independence relations of the mixture distribution. We then consider the problem of structure learning based on samples from such distributions. Since the mixing variable is latent, we consider causal structure discovery algorithms such as FCI that can deal with latent variables. We show that such algorithms recover a "union" of the component DAGs and can identify variables whose conditional distribution across the component DAGs vary. We demonstrate our results on synthetic and real data showing that the inferred graph identifies nodes that vary between the different mixture components. As an immediate application, we demonstrate how retrieval of this causal information can be used to cluster samples according to each mixture component.

Key facts. Cited by 24 papers in the map (data through 13 Oct 2026). First posted on arXiv 31 Jan 2020.

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2021: 5; 2022: 2; 2023: 5; 2024: 5; 2025: 3; 2026: 4.

24 papers.

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  1. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  2. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  3. 2024Federated Causal Discovery from Heterogeneous Datacited by 15
  4. 2021A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populationscited by 10
  5. 2023Discovering Mixtures of Structural Causal Models from Time Series Datacited by 10
  6. 2022Disentangling Mixtures of Unknown Causal Interventionscited by 6
  7. 2021Collaborative Causal Discovery with Atomic Interventionscited by 5
  8. 2023iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Modelscited by 5
  9. 2023Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representationcited by 4
  10. 2023Causal Discovery in Mixtures of Populationscited by 4
  11. 2024Interventional Causal Discovery in a Mixture of DAGscited by 3
  12. 2021CCSL: A Causal Structure Learning Method from Multiple Unknown Environmentscited by 2
12 more
  1. 2023Causal Temporal Regime Structure Learningcited by 2
  2. 2025Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphscited by 2
  3. 2021NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameterscited by 1
  4. 2024Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Supportcited by 1
  5. 2025Covariate Dependent Mixture of Bayesian Networkscited by 1
  6. 2025Learning Causal Structure Distributions for Robust Planningcited by 1
  7. 2026Identifiable Markov Switching Models with Instantaneous Effects and Exponential Familiescited by 1
  8. 2021Fine-Grained System Identification of Nonlinear Neural Circuitscited by 0
  9. 2024Learning Mixtures of Unknown Causal Interventionscited by 0
  10. 2026Heterogeneous Ordinal Structure Learning with Bayesian Nonparametric Complexity Discoverycited by 0
  11. 2026Detecting Changes in Causal Dependence with Kernels and Copulascited by 0
  12. 2026Structure Learning on Clustered Datacited by 0