Dynamic Structural Causal Modeling for Sleep (arXiv 2608.20285)

Forum topic · 小凯 · 2026-08-22

Summary

This paper, Dynamic Structural Causal Modeling for Sleep by Ranveer Singh, Saurabh Mathur, Pranuthi Tenali, and Arun Badi (arXiv 2608.20285, released 2026-08-22), applies causal discovery to sleep-disordered breathing (SDB). The authors learn dynamic causal graphs from home sleep apnea test (HSAT) recordings using the PCMCI+ algorithm on windowed score variables derived from 105 HSAT records. Domain knowledge is incorporated via edge blacklists, and bootstrap aggregation addresses the small size of patient subgroups. The learned graphs reveal systematic differences in causal structure across sex and age subgroups. Temporal self-dependencies and the apnea-desaturation relationship persist across all cohorts, while other relationships vary considerably. These findings offer a causal perspective on the heterogeneity of sleep-disordered breathing and may guide the development of more personalized intervention strategies.

Paper Overview

Research Area: Machine Learning Authors: Ranveer Singh, Saurabh Mathur, Pranuthi Tenali, Arun Badi Published: 2026-08-22 arXiv: 2608.20285

Abstract

The causal dynamics of sleep-disordered breathing (SDB) are complex and vary across patient populations, hindering the development of targeted interventions. This paper learns dynamic causal graphs of SDB from home sleep apnea test (HSAT) recordings, revealing systematic differences in causal structure between sex and age subgroups.

The authors use the PCMCI+ algorithm on windowed score variables derived from 105 HSAT records, incorporating domain knowledge through edge blacklists and employing bootstrap aggregation to address small subgroup sizes.

Key Findings

Temporal self-dependencies and the apnea-desaturation relationship persist across all cohorts.
Other causal relationships vary considerably between subgroups.
The learned graphs provide a causal perspective on SDB heterogeneity, potentially guiding more personalized treatment strategies.

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