Analyzing the Temporal Factors for Anxiety and Depression Symptoms with the Rashomon Perspective

TL;DR

This study employs the Rashomon perspective with random forest models to analyze the stability of predictors for anxiety and depression, revealing consistent demographic effects and significant temporal fluctuations, emphasizing the importance of considering multiple models for robust mental health insights.

Contribution

It introduces a Rashomon-based approach to interpret multiple models in mental health prediction, highlighting the importance of model multiplicity for robust insights.

Findings

Demographic variables like age, sex, and education influence anxiety and depression risk.

Temporal effects such as diurnal and circaseptan fluctuations significantly impact mental health risk.

Moving beyond a single best model enhances the robustness of psychological data interpretation.

Abstract

This paper introduces a new modeling perspective in the public mental health domain to provide a robust interpretation of the relations between anxiety and depression, and the demographic and temporal factors. This perspective particularly leverages the Rashomon Effect, where multiple models exhibit similar predictive performance but rely on diverse internal structures. Instead of considering these multiple models, choosing a single best model risks masking alternative narratives embedded in the data. To address this, we employed this perspective in the interpretation of a large-scale psychological dataset, specifically focusing on the Patient Health Questionnaire-4. We use a random forest model combined with partial dependence profiles to rigorously assess the robustness and stability of predictive relationships across the resulting Rashomon set, which consists of multiple models that exhibit similar predictive performance. Our findings confirm that demographic variables \texttt{age}, \texttt{sex}, and \texttt{education} lead to consistent structural shifts in anxiety and depression risk. Crucially, we identify significant temporal effects: risk probability demonstrates clear diurnal and circaseptan fluctuations, peaking during early morning hours. This work demonstrates the necessity of moving beyond the best model to analyze the entire Rashomon set. Our results highlight that the observed variability, particularly due to circadian and circaseptan rhythms, must be meticulously considered for robust interpretation in psychological screening. We advocate for a multiplicity-aware approach to enhance the stability and generalizability of ML-based conclusions in mental health research.