Dynamic Structural Causal Modeling for Sleep
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Abstract (arXiv)
The causal dynamics of sleep-disordered breathing are complex and vary across patient populations, hindering the development of targeted interventions. We learn dynamic causal graphs of sleep-disordered breathing from Home Sleep Apnea Test (HSAT) recordings, revealing systematic differences in causal structure across sex and age subcohorts. We do so using the PCMCI+ algorithm on windowed fractional variables derived from 105 HSAT recordings, exploiting domain knowledge via edge blacklisting and employing bootstrap aggregation to address small subcohort sizes. The learned graphs show that temporal self-dependencies and the apnea-desaturation relationship persist across all cohorts, while other relationships vary substantially.
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References · 7
- 2017Detecting causal associations in large nonlinear time series datasetscited by 244
- 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
- 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 137
- 2013Data Analysis with Bayesian Networks: A Bootstrap Approachcited by 32
- 2020Reconstructing regime-dependent causal relationships from observational time seriescited by 28
- 2023Bootstrap aggregation and confidence measures to improve time series causal discoverycited by 8
- 2025Causal Discovery on Higher-Order Interactionscited by 2
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