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Functional Bayesian networks for discovering causality from multivariate functional data

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  • Fangting Zhou
  • Kejun He
  • Kunbo Wang
  • Yanxun Xu
  • Yang Ni

Abstract

Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest. In this paper, we develop a novel Bayesian network (BN) model for multivariate functional data where conditional independencies and causal structure are encoded by a directed acyclic graph. Specifically, we allow the functional objects to deviate from Gaussian processes, which is the key to unique causal structure identification even when the functions are measured with noises. A fully Bayesian framework is designed to infer the functional BN model with natural uncertainty quantification through posterior summaries. Simulation studies and real data examples demonstrate the practical utility of the proposed model.

Suggested Citation

  • Fangting Zhou & Kejun He & Kunbo Wang & Yanxun Xu & Yang Ni, 2023. "Functional Bayesian networks for discovering causality from multivariate functional data," Biometrics, The International Biometric Society, vol. 79(4), pages 3279-3293, December.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:4:p:3279-3293
    DOI: 10.1111/biom.13922
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    References listed on IDEAS

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