这篇论文揭示了睡眠呼吸障碍的复杂因果结构，使用PCMCI+算法从HSAT记录中学习动态因果图，对睡眠障碍的研究具有重要意义。
通过PCMCI+算法从105次家庭睡眠呼吸暂停测试（HSAT）记录中学习睡眠呼吸障碍的动态因果图，揭示不同性别和年龄亚群在因果结构上的系统性差异。学习到的图表明，时间自我依赖和呼吸暂停-低氧血症关系在所有群体中持续存在，而其他关系存在显著差异。
Dynamic Structural Causal Modeling for Sleep
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.