{
  "abstract": "Causal Bayesian Optimization (CBO) combines causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems with causal structure. This survey provides a systematic review of CBO through a unified BO-loop perspective, showing how causal assumptions shape intervention search spaces, surrogate models, acquisition functions, and decision policies. We organize existing methods by graph and system-knowledge assumptions, environment, intervention representation, surrogate architecture, and decision rule, and connect CBO to causal bandits, Bayesian experimental design, safe optimization, policy search, and causal abstraction. We also introduce a reproducibility-oriented benchmark spanning hard- and soft-intervention settings, with standardized GAP and a new trajectory-aware Path-Aware GAP (PA-GAP), evaluating seven CBO methods and a non-causal BO baseline across thirteen datasets, three budgets, and two metrics. Results show that no method dominates uniformly: rankings depend on dataset, budget, metric, and how causal information is used, while strong non-causal baselines remain competitive in several settings. Controlled graph-misspecification and omitted-variable stress tests further show that rankings can change substantially when learner-side causal information is perturbed. We conclude by identifying key open challenges, including robustness to causal-assumption violations, scalable unknown-graph optimization, mixed intervention types, realistic cost models, stronger theoretical guarantees, and integration with modern representation learning and causal abstractions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: Transactions on Machine Learning Research, 2026",
  "arxiv": "2609.24112",
  "author_total": 4,
  "authors": [
    {
      "name": "Chenfeng Huang"
    },
    {
      "name": "Thuy T. Le"
    },
    {
      "name": "Zixuan Ma"
    },
    {
      "name": "Hien Tran"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/azb4k2zy22z2k.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.24112",
  "fulltext_url": null,
  "id": "azb4k2zy22z2k",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.24112",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1803|Advanced Bandit Algorithms Research"
    ]
  ],
  "openalex": "W7214025865",
  "pdf_url": "https://arxiv.org/pdf/2609.24112",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/azb4k2zy22z2k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:33Z",
      "native_id": "2609.24112",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.24112"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Causal Bayesian Optimization: Foundations, Methods, and Applications",
  "title_text": "Causal Bayesian Optimization: Foundations, Methods, and Applications",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:33Z",
  "url": "https://research.draconitic.com/paper/azb4k2zy22z2k",
  "venue": "arXiv",
  "versions": []
}