mlsanityData to 7 Oct 2026

Interventions, Where and How? Experimental Design for Causal Models at Scale

arXiv 2203.02016 ↗First posted 3 Mar 2022cs.LG cs.AI stat.MLcited by 49

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Abstract (arXiv)

Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability: factors that introduce uncertainty in estimating the underlying structural causal model (SCM). Selecting experiments (interventions) based on the uncertainty arising from both factors can expedite the identification of the SCM. Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target. This work incorporates recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework, allowing for active causal discovery of large, nonlinear SCMs while selecting both the interventional target and the value. We demonstrate the performance of the proposed method on synthetic graphs (Erdos-R`enyi, Scale Free) for both linear and nonlinear SCMs as well as on the in-silico single-cell gene regulatory network dataset, DREAM.

Key facts. Cited by 49 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 3 Mar 2022.

Builds on

29 of 68 references.

References · 29

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2019On Variational Bounds of Mutual Informationcited by 550
  3. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  4. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 498
  5. 2019BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learningcited by 428
  6. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  7. 2018Learning Latent Permutations with Gumbel-Sinkhorn Networkscited by 172
  8. 2019A Meta-Transfer Objective for Learning to Disentangle Causal Mechanismscited by 158
  9. 2019Variational Bayesian Optimal Experimental Designcited by 116
  10. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
  11. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  12. 2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 103
17 more
  1. 2017On Nesting Monte Carlo Estimatorscited by 82
  2. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
  3. 2015Learning Causal Graphs with Small Interventionscited by 63
  4. 2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
  5. 2019A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experimentscited by 58
  6. 2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
  7. 2017Cost-Optimal Learning of Causal Graphscited by 44
  8. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  9. 2021Learning Neural Causal Models with Active Interventionscited by 40
  10. 2020Active Structure Learning of Causal DAGs via Directed Clique Treecited by 38
  11. 2020Active Invariant Causal Prediction: Experiment Selection through Stabilitycited by 37
  12. 2018Efficient Bayesian Experimental Design for Implicit Modelscited by 34
  13. 2013Data Analysis with Bayesian Networks: A Bootstrap Approachcited by 31
  14. 2021Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Datacited by 27
  15. 2019Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networkscited by 18
  16. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  17. 2021Matching a Desired Causal State via Shift Interventionscited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 13; 2023: 8; 2024: 11; 2025: 9; 2026: 8.

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  1. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  2. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  3. 2022Active Bayesian Causal Inferencecited by 34
  4. 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
  5. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  6. 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 30
  7. 2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
  8. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  9. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  10. 2022Causal Entropy Optimizationcited by 13
  11. 2022A Meta-Reinforcement Learning Algorithm for Causal Discoverycited by 11
  12. 2024Amortized Active Causal Induction with Deep Reinforcement Learningcited by 10
37 more
  1. 2025Can Large Language Models Help Experimental Design for Causal Discovery?cited by 10
  2. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  3. 2022On the Generalization and Adaption Performance of Causal Modelscited by 8
  4. 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
  5. 2023Intervention Generalization: A View from Factor Graph Modelscited by 7
  6. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
  7. 2023Constrained Causal Bayesian Optimizationcited by 6
  8. 2024CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learningcited by 6
  9. 2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
  10. 2022Learning Latent Structural Causal Modelscited by 5
  11. 2022Trust Your nabla: Gradient-based Intervention Targeting for Causal Discoverycited by 5
  12. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  13. 2023Sequential Underspecified Instrument Selection for Cause-Effect Estimationcited by 4
  14. 2023CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Designcited by 4
  15. 2025CausalMan: A physics-based simulator for large-scale causalitycited by 4
  16. 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Modelscited by 4
  17. 2023Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimationcited by 3
  18. 2024Causal Discovery under Off-Target Interventionscited by 2
  19. 2024Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experimentscited by 2
  20. 2025Characterization and Learning of Causal Graphs from Hard Interventionscited by 2
  21. 2026Can Neural Networks Learn by Experimenting on Themselves? Self-Interventional Learning from Functional Consequences to Predictive Self-Knowledgecited by 2
  22. 2022Latent Variable Models for Bayesian Causal Discoverycited by 1
  23. 2022Federated Causal Discovery From Interventionscited by 1
  24. 2024Graph Agnostic Causal Bayesian Optimisationcited by 1
  25. 2025CORD: Generalizable Cooperation via Role Diversitycited by 1
  26. 2025Goal-Oriented Sequential Bayesian Experimental Design for Causal Learningcited by 1
  27. 2022Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selectioncited by 0
  28. 2024Bayesian Intervention Optimization for Causal Discoverycited by 0
  29. 2025Efficient Data Selection for Training Genomic Perturbation Modelscited by 0
  30. 2025Design of Experiment for Discovering Directed Mixed Graphcited by 0
  31. 2026Active Causal Experimentalist (ACE): Learning Intervention Strategies via Direct Preference Optimizationcited by 0
  32. 2026Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Datacited by 0
  33. 2026Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Sciencecited by 0
  34. 2026Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertaintycited by 0
  35. 2026Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the 1+K Lower Boundcited by 0
  36. 2026FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventionscited by 0
  37. 2026Certifying Interventional Agreement Among Observationally Equivalent Causal Modelscited by 0