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<p>Digital phenotyping, the moment-by-moment quantification of human behavior using data from personal devices and sensors, has shown great promise in predicting mental health outcomes. However, the field is reaching a ’predictive plateau,’ where models, while accurate, are often opaque black boxes that offer limited insight into underlying mechanisms of well-being. This paper proposes a fundamental paradigm shift from predictive classification to structural causal modeling. We introduce a two-stage computational framework that first learns unified daily behavioral embeddings from multimodal sensor data using a CNN-based encoder, and then applies neuro-symbolic causal discovery to infer interpretable directed graphs of behavioral–psychological dynamics. In our evaluation, we observed clear signs of this predictive plateau: even deep embedding models performed only slightly better than chance in stress prediction (best AUC = 0.532). By comparison, the causal approach identified candidate time-lagged associations; for example, lower levels of sleep activity (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(p&lt;0.01\)</EquationSource></InlineEquation>) and reduced mobility (<InlineEquation ID="IEq2"><EquationSource Format="TEX">\(p&lt;0.05\)</EquationSource></InlineEquation>) often appeared as preceding indicators of stress episodes. Dimensionality reduction via PCA retained five principal components explaining approximately 85% of the variance, enabling post-hoc interpretation of candidate behavioral components such as “Stationary Social Engagement.” We define these components and their associated edge weights as candidate causal biomarkers: hypothesis-generating indicators of possible lagged behavior–stress relationships, rather than confirmed interventional causal effects.</p>

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A causal discovery framework for digital phenotyping

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Ahmed Ibrahim

摘要

Digital phenotyping, the moment-by-moment quantification of human behavior using data from personal devices and sensors, has shown great promise in predicting mental health outcomes. However, the field is reaching a ’predictive plateau,’ where models, while accurate, are often opaque black boxes that offer limited insight into underlying mechanisms of well-being. This paper proposes a fundamental paradigm shift from predictive classification to structural causal modeling. We introduce a two-stage computational framework that first learns unified daily behavioral embeddings from multimodal sensor data using a CNN-based encoder, and then applies neuro-symbolic causal discovery to infer interpretable directed graphs of behavioral–psychological dynamics. In our evaluation, we observed clear signs of this predictive plateau: even deep embedding models performed only slightly better than chance in stress prediction (best AUC = 0.532). By comparison, the causal approach identified candidate time-lagged associations; for example, lower levels of sleep activity (\(p<0.01\)) and reduced mobility (\(p<0.05\)) often appeared as preceding indicators of stress episodes. Dimensionality reduction via PCA retained five principal components explaining approximately 85% of the variance, enabling post-hoc interpretation of candidate behavioral components such as “Stationary Social Engagement.” We define these components and their associated edge weights as candidate causal biomarkers: hypothesis-generating indicators of possible lagged behavior–stress relationships, rather than confirmed interventional causal effects.