mlsanityData to 14 Oct 2026

Constrained Causal Bayesian Optimization

arXiv 2305.20011 ↗First posted 31 May 2023stat.ML cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

We propose constrained causal Bayesian optimization (cCBO), an approach for finding interventions in a known causal graph that optimize a target variable under some constraints. cCBO first reduces the search space by exploiting the graph structure and, if available, an observational dataset; and then solves the restricted optimization problem by modelling target and constraint quantities using Gaussian processes and by sequentially selecting interventions via a constrained expected improvement acquisition function. We propose different surrogate models that enable to integrate observational and interventional data while capturing correlation among effects with increasing levels of sophistication. We evaluate cCBO on artificial and real-world causal graphs showing successful trade off between fast convergence and percentage of feasible interventions.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 19 of its 50 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

19 of 50 references.

References · 19

  1. 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functionscited by 204
  2. 2016Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Roboticscited by 134
  3. 2014Bayesian Optimization with Unknown Constraintscited by 109
  4. 2017Constrained Bayesian Optimization with Noisy Experimentscited by 104
  5. 2016Causal Bandits: Learning Good Interventions via Causal Inferencecited by 101
  6. 2020Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planningcited by 86
  7. 2020Scalable Constrained Bayesian Optimizationcited by 84
  8. 2018Stagewise Safe Bayesian Optimization with Gaussian Processescited by 79
  9. 2015Safe Controller Optimization for Quadrotors with Gaussian Processescited by 78
  10. 2019Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspacescited by 73
  11. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  12. 2020Causal Bayesian Optimizationcited by 39
7 more
  1. 2016Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangiancited by 29
  2. 2022Model-based Causal Bayesian Optimizationcited by 26
  3. 2015A Bayesian approach to constrained single- and multi-objective optimizationcited by 23
  4. 2017Constrained Bayesian Optimization for Automatic Chemical Designcited by 20
  5. 2022Causal Entropy Optimizationcited by 13
  6. 2021JUMBO: Scalable Multi-task Bayesian Optimization using Offline Datacited by 8
  7. 2020Learning to search efficiently for causally near-optimal treatmentscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 2.

6 papers.

  1. 2024CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Seriescited by 3
  2. 2025Multi-Objective Causal Bayesian Optimizationcited by 3
  3. 2026Multi-Objective Multi-Fidelity Bayesian Optimization with Causal Priorscited by 2
  4. 2023Bayesian Optimization of Expensive Nested Grey-Box Functionscited by 1
  5. 2025Causal Bayesian Optimization with Unknown Graphscited by 0
  6. 2026Transferring Information Across Interventions in Causal Bayesian Optimizationcited by 0