Summary
This paper (arXiv:2608.20285) by Ranveer Singh, Saurabh Mathur, Pranuthi Tenali, and Arun Badi applies dynamic structural causal modeling to sleep-disordered breathing. The authors learn causal graphs directly from home sleep apnea test (HSAT) recordings to characterize the complex and heterogeneous causal dynamics of sleep apnea across patient populations. Using the PCMCI+ algorithm on windowed score variables derived from 105 HSAT records, the approach incorporates domain knowledge via edge blacklisting and employs bootstrap aggregation to address small subgroup sample sizes. The learned graphs reveal systematic differences in causal structure across sex and age subgroups. Temporal self-dependencies and the apnea–desaturation relationship persist consistently across all cohorts, whereas other relationships vary substantially. These findings provide a causal perspective on the heterogeneity of sleep-disordered breathing and may guide the development of more personalized intervention and treatment strategies.
Paper Overview
- Field: Machine Learning
- Authors: Ranveer Singh, Saurabh Mathur, Pranuthi Tenali, Arun Badi
- Published: 2026-08-22
- arXiv: 2608.20285
Summary
Sleep-disordered breathing (SDB) exhibits complex causal dynamics that vary across patient populations, hindering the development of targeted interventions. This work learns dynamic causal graphs of SDB directly from home sleep apnea test (HSAT) recordings, revealing systematic differences in causal structure between sex and age subgroups.
Methodology
- The PCMCI+ causal discovery algorithm is applied to windowed score variables derived from 105 HSAT records.
- Domain knowledge is incorporated through an edge blacklist.
- Bootstrap aggregation is used to address small subgroup sample sizes.
Findings
- Temporal self-dependencies and the apnea–desaturation relationship persist consistently across all cohorts.
- Other causal relationships vary substantially across patient subgroups.
- The results offer a causal perspective on SDB heterogeneity and may guide more personalized treatment strategies.
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