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Discovering causal structures in Bayesian Gaussian directed acyclic graph models

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  • Federico Castelletti
  • Guido Consonni

Abstract

Causal directed acyclic graphs (DAGs) are naturally tailored to represent biological signalling pathways. However, a causal DAG is only identifiable up to Markov equivalence if only observational data are available. Interventional data, based on exogenous perturbations of the system, can greatly improve identifiability. Since the gain of an intervention crucially depends on the intervened variables, a natural issue is devising efficient strategies for optimal causal discovery. We present a Bayesian active learning procedure for Gaussian DAGs which requires no subjective specification on the side of the user, explicitly takes into account the uncertainty on the space of equivalence classes (through the posterior distribution) and sequentially proposes the choice of the optimal intervention variable. In simulation experiments our method, besides surpassing designs based on a random choice of intervention nodes, shows decisive improvements over currently available algorithms and is competitive with the best alternative benchmarks. An important reason behind this strong performance is that, unlike non‐Bayesian algorithms, our utility function naturally incorporates graph estimation uncertainty through the posterior edge inclusion probability. We also reanalyse the Sachs data on protein signalling pathways from an active learning perspective and show that DAG identification can be achieved by using only a subset of the available intervention samples.

Suggested Citation

  • Federico Castelletti & Guido Consonni, 2020. "Discovering causal structures in Bayesian Gaussian directed acyclic graph models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1727-1745, October.
  • Handle: RePEc:bla:jorssa:v:183:y:2020:i:4:p:1727-1745
    DOI: 10.1111/rssa.12550
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    References listed on IDEAS

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    1. Judea Pearl, 2003. "Statistics and causal inference: A review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(2), pages 281-345, December.
    2. Jonas Peters & Peter Bühlmann & Nicolai Meinshausen, 2016. "Causal inference by using invariant prediction: identification and confidence intervals," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 947-1012, November.
    3. Alain Hauser & Peter Bühlmann, 2015. "Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 291-318, January.
    4. J. Peters & P. Bühlmann, 2014. "Identifiability of Gaussian structural equation models with equal error variances," Biometrika, Biometrika Trust, vol. 101(1), pages 219-228.
    5. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.
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    Cited by:

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    2. Federico Castelletti & Guido Consonni & Luca Rocca, 2022. "Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 261-267, June.
    3. Wenjun Xie & Qingyuan Yu & Wen Fang & Xiaoge Zhang & Jinghua Geng & Jiayi Tang & Wenfei Jing & Miaomiao Liu & Zongwei Ma & Jianxun Yang & Jun Bi, 2024. "Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
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    5. Federico Castelletti & Alessandro Mascaro, 2021. "Structural learning and estimation of joint causal effects among network-dependent variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1289-1314, December.

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