Uncertainties in Modeling Psychological Symptom Networks The Case of Suicide

In psychological research, network models are widely used to study symptoms of mental health disorders. However, these models often fail to account for uncertainty, leading to potentially misleading inferences. To address this issue, this study examines the robustness of psychological networks by analyzing a dataset of risk factors for suicidal behavior with multiple network algorithms. We compare two causal discovery algorithms—Hill Climbing (HC) and TABU search—and the Gaussian Graphical Model (GGM), a widely used statistical network model in psychology. Uncertainty is assessed along two dimensions: (1) the impact of noise, by introducing varying levels of white noise into the dataset, and (2) the effect of sample size reduction, by systematically decreasing the number of observations. Our results indicate that both HC and TABU search are highly sensitive to noise and sample size, with HC slightly outperforming TABU in terms of precision and recall. GGM performance declines gradually with increasing noise and sample size reduction, leading to sparser networks. For all algorithms, recall declined at a faster rate than precision. Finally, we examine the robustness of edges leading to suicidal ideation, finding that the edge from Depression to suicidal ideation remains relatively stable across conditions. This is a promising result, since many suicide interventions are based on treating depressive mood. Our results emphasize the importance of considering uncertainty in network-based psychological research, particularly when applying causal discovery algorithms.