mlsanityData to 7 Oct 2026

Learning Neural Causal Models with Active Interventions

arXiv 2109.02429 ↗First posted 6 Sep 2021stat.ML cs.LGcited by 40

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Abstract (arXiv)

Discovering causal structures from data is a challenging inference problem of fundamental importance in all areas of science. The appealing properties of neural networks have recently led to a surge of interest in differentiable neural network-based methods for learning causal structures from data. So far, differentiable causal discovery has focused on static datasets of observational or fixed interventional origin. In this work, we introduce an active intervention targeting (AIT) method which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting and is applicable for both discrete and continuous optimization formulations of learning the underlying directed acyclic graph (DAG) from data. We examine the proposed method across multiple frameworks in a wide range of settings and demonstrate superior performance on multiple benchmarks from simulated to real-world data.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 29 of its 68 references that are in the map. First posted on arXiv 6 Sep 2021.

Builds on

29 of 68 references.

References · 29

  1. 2015Variational Inference with Normalizing Flowscited by 2,161
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  4. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  5. 2019Gradient-Based Neural DAG Learningcited by 227
  6. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  7. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  8. 2019Learning Sparse Nonparametric DAGscited by 207
  9. 2019Causality matters in medical imagingcited by 193
  10. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  11. 2019A Meta-Transfer Objective for Learning to Disentangle Causal Mechanismscited by 158
  12. 2019Causal Discovery with Reinforcement Learningcited by 145
17 more
  1. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
  2. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  3. 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 97
  4. 2017Causal Structure Learningcited by 84
  5. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  6. 2015Learning Causal Graphs with Small Interventionscited by 63
  7. 2019A Graph Autoencoder Approach to Causal Structure Learningcited by 55
  8. 2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
  9. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  10. 2017Cost-Optimal Learning of Causal Graphscited by 44
  11. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  12. 2017Budgeted Experiment Design for Causal Structure Learningcited by 38
  13. 2020Active Structure Learning of Causal DAGs via Directed Clique Treecited by 38
  14. 2020Active Invariant Causal Prediction: Experiment Selection through Stabilitycited by 37
  15. 2018Experimental Design for Cost-Aware Learning of Causal Graphscited by 25
  16. 2019Interventional Experiment Design for Causal Structure Learningcited by 8
  17. 2021Bayesian Optimal Experimental Design for Inferring Causal Structurecited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 18; 2023: 5; 2024: 5; 2025: 5; 2026: 5.

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  1. 2022Learning to Induce Causal Structurecited by 68
  2. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  3. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  4. 2022Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Modelscited by 42
  5. 2022Active Bayesian Causal Inferencecited by 34
  6. 2022Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoningcited by 33
  7. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 32
  8. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  9. 2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
  10. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  11. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  12. 2024Causal Composition Diffusion Model for Closed-loop Traffic Generationcited by 22
28 more
  1. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  2. 2021FedDAG: Federated DAG Structure Learningcited by 17
  3. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  4. 2022Towards Fine-grained Causal Reasoning and QAcited by 13
  5. 2022Unicorn: Reasoning about Configurable System Performance through the lens of Causalitycited by 12
  6. 2022A Meta-Reinforcement Learning Algorithm for Causal Discoverycited by 11
  7. 2025Can Large Language Models Help Experimental Design for Causal Discovery?cited by 10
  8. 2022On the Generalization and Adaption Performance of Causal Modelscited by 8
  9. 2022Neural Design for Genetic Perturbation Experimentscited by 6
  10. 2024CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learningcited by 6
  11. 2022Learning Latent Structural Causal Modelscited by 5
  12. 2022Trust Your nabla: Gradient-based Intervention Targeting for Causal Discoverycited by 5
  13. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  14. 2023DiscoGen: Learning to Discover Gene Regulatory Networkscited by 3
  15. 2023Multi-omics Prediction from High-content Cellular Imaging with Deep Learningcited by 3
  16. 2024BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learningcited by 3
  17. 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3
  18. 2025IGDA: Interactive Graph Discovery through Large Language Model Agentscited by 3
  19. 2025Measure-Theoretic Anti-Causal Representation Learningcited by 3
  20. 2022Intrinsically Motivated Learning of Causal World Modelscited by 2
  21. 2022Latent Variable Models for Bayesian Causal Discoverycited by 1
  22. 2025Causality in the human niche: lessons for machine learningcited by 1
  23. 2025DODO: Causal Structure Learning with Budgeted Interventionscited by 1
  24. 2026Why LLMs Fail at Causal Discovery and How Interventional Agents Escapecited by 1
  25. 2026Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Sciencecited by 0
  26. 2026Adversarial Causal Intervention Falsificationcited by 0
  27. 2026FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventionscited by 0
  28. 2026Structure-agnostic Causal Representation Learningcited by 0