ArticleBiometrics2023
Functional Bayesian networks for discovering causality from multivariate functional data.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Scalar-Function Causal Discovery for Generating Causal Hypotheses with Observational Wearable Device Data.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data.Advances in neural information processing systems · 2023Article
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Authors and funding
5 authors.
Funding
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.
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Registered trials
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