mlsanityData to 14 Oct 2026

Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks

arXiv 1910.03962 ↗First posted 9 Oct 2019stat.ML cs.LGcited by 18

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Abstract (arXiv)

We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform those experiments which, in expectation with respect to the current model, are maximally informative about the underlying causal structure. Unlike previous work, we consider the setting of continuous random variables with non-linear functional relationships, modelled with Gaussian process priors. To address the arising problem of choosing from an uncountable set of possible interventions, we propose to use Bayesian optimisation to efficiently maximise a Monte Carlo estimate of the expected information gain.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Oct 2019.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 4; 2024: 7; 2025: 2.

18 papers.

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  1. 2023Modern Bayesian Experimental Designcited by 125
  2. 2021DiBS: Differentiable Bayesian Structure Learningcited by 106
  3. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  4. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  5. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  6. 2024InfoNCE: Identifying the Gap Between Theory and Practicecited by 40
  7. 2022Active Bayesian Causal Inferencecited by 35
  8. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  9. 2022Causal Entropy Optimizationcited by 13
  10. 2024Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environmentcited by 12
  11. 2024Stopping Bayesian Optimization with Probabilistic Regret Boundscited by 11
  12. 2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
6 more
  1. 2024Targeted Sequential Indirect Experiment Designcited by 4
  2. 2025JADAI: Jointly Amortizing Adaptive Design and Bayesian Inferencecited by 4
  3. 2024Bayesian Causal Inference with Gaussian Process Networkscited by 3
  4. 2025Goal-Oriented Sequential Bayesian Experimental Design for Causal Learningcited by 2
  5. 2024Graph Agnostic Causal Bayesian Optimisationcited by 1
  6. 2023Estimation of Counterfactual Interventions under Uncertaintiescited by 0