mlsanityData to 7 Oct 2026

BaCaDI: Bayesian Causal Discovery with Unknown Interventions

arXiv 2206.01665 ↗First posted 3 Jun 2022cs.LG stat.ME stat.MLcited by 26

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Abstract (arXiv)

Inferring causal structures from experimentation is a central task in many domains. For example, in biology, recent advances allow us to obtain single-cell expression data under multiple interventions such as drugs or gene knockouts. However, the targets of the interventions are often uncertain or unknown and the number of observations limited. As a result, standard causal discovery methods can no longer be reliably used. To fill this gap, we propose a Bayesian framework (BaCaDI) for discovering and reasoning about the causal structure that underlies data generated under various unknown experimental or interventional conditions. BaCaDI is fully differentiable, which allows us to infer the complex joint posterior over the intervention targets and the causal structure via efficient gradient-based variational inference. In experiments on synthetic causal discovery tasks and simulated gene-expression data, BaCaDI outperforms related methods in identifying causal structures and intervention targets.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 35 of its 81 references that are in the map. First posted on arXiv 3 Jun 2022.

Builds on

35 of 81 references.

References · 35

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23 more
  1. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  2. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  3. 2017Stein Variational Gradient Descent as Gradient Flowcited by 149
  4. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
  5. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
  6. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  7. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  8. 2018Characterizing and Learning Equivalence Classes of Causal DAGs under Interventionscited by 76
  9. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  10. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
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  12. 2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
  13. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  14. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  15. 2013Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributionscited by 41
  16. 2021Learning Neural Causal Models with Active Interventionscited by 40
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  18. 2017Learning Causal Structures Using Regression Invariancecited by 32
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  23. 2022The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 9; 2025: 6; 2026: 5.

26 papers.

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  1. 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
  2. 2024Sample, estimate, aggregate: A recipe for causal discovery foundation modelscited by 19
  3. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  4. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  5. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  6. 2025Towards Interpretable Deep Generative Models via Causal Representation Learningcited by 12
  7. 2023Constraint-Free Structure Learning with Smooth Acyclic Orientationscited by 10
  8. 2022Learning Latent Structural Causal Modelscited by 5
  9. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  10. 2024Multi-task Heterogeneous Graph Learning on Electronic Health Recordscited by 5
  11. 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventionscited by 4
  12. 2024Generative Intervention Models for Causal Perturbation Modelingcited by 4
14 more
  1. 2023Bayesian causal discovery from unknown general interventionscited by 2
  2. 2024Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experimentscited by 2
  3. 2025Causal Effect Identification in Heterogeneous Environments from Higher-Order Momentscited by 2
  4. 2026Test-Time Learning of Causal Structure from Interventional Datacited by 2
  5. 2026PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modellingcited by 2
  6. 2024Identifying biological perturbation targets through causal differential networkscited by 1
  7. 2025Bayesian Hierarchical Invariant Predictioncited by 1
  8. 2025MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventionscited by 1
  9. 2023Learning Linear Gaussian Polytree Models with Interventionscited by 0
  10. 2025Causal Bayesian Optimization with Unknown Graphscited by 0
  11. 2025Large-Scale Bayesian Causal Discovery with Interventional Datacited by 0
  12. 2026SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targetscited by 0
  13. 2026Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applicationscited by 0
  14. 2026Amortized Bayesian Causal Discovery of Extended Factor Graphscited by 0