Scalar-Function Causal Discovery for Generating Causal Hypotheses with Observational Wearable Device Data.

ABSTRACT: Digital health technologies such as wearable devices have transformed health data analytics, providing continuous, high-resolution functional data on various health metrics, thereby opening new avenues for innovative research. In this work, we introduce a new approach for generating causal hypotheses for a pair of a continuous functional variable (e.g., physical activities recorded over time) and a binary scalar variable (e.g., mobility condition indicator). Our method goes beyond traditional association-focused approaches and has the potential to reveal the underlying causal mechanism. We theoretically show that the proposed scalar-function causal model is identifiable with observational data alone. Our identifiability theory justifies the use of a simple yet principled algorithm to disce

SUBMITTER: Rogovchenko V 

PROVIDER: S-EPMC10764070 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature