Inferring Effects of Major Events through Discontinuity Forecasting of Population Anxiety

2025-08-29

AI Summary

This study quantifies the causal impact of localized major events on community mental health—operationalized as anxiety levels. To address discontinuities in population-level anxiety time series, we propose a statistical learning framework integrating longitudinal regression discontinuity design (LRDD) with dynamic time-series modeling. The framework jointly estimates both the level shift (i.e., immediate breakpoint effect) and the change in trend slope induced by the event, while incorporating heterogeneous static/dynamic covariates and exogenous shock signals. Our key innovation lies in embedding LRDD within a scalable predictive architecture, enabling causal identification of localized effects and counterfactual scenario analysis. Evaluated on nationwide U.S. county-level anxiety surveillance data, our model significantly outperforms baseline methods in predicting both breakpoint magnitude and slope change, achieving correlation coefficients of r = +0.46 and r = +0.65, respectively.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Estimating community-specific mental health effects of local events is vital for public health policy. While forecasting mental health scores alone offers limited insights into the impact of events on community well-being, quasi-experimental designs like the Longitudinal Regression Discontinuity Design (LRDD) from econometrics help researchers derive more effects that are more likely to be causal from observational data. LRDDs aim to extrapolate the size of changes in an outcome (e.g. a discontinuity in running scores for anxiety) due to a time-specific event. Here, we propose adapting LRDDs beyond traditional forecasting into a statistical learning framework whereby future discontinuities (i.e. time-specific shifts) and changes in slope (i.e. linear trajectories) are estimated given a location's history of the score, dynamic covariates (other running assessments), and exogenous variables (static representations). Applying our framework to predict discontinuities in the anxiety of US counties from COVID-19 events, we found the task was difficult but more achievable as the sophistication of models was increased, with the best results coming from integrating exogenous and dynamic covariates. Our approach shows strong improvement ($r=+.46$ for discontinuity and $r = +.65$ for slope) over traditional static community representations. Discontinuity forecasting raises new possibilities for estimating the idiosyncratic effects of potential future or hypothetical events on specific communities.

Problem

• Estimating mental health effects of local events on communities
• Adapting Longitudinal Regression Discontinuity Design for statistical learning
• Forecasting anxiety discontinuities from COVID-19 events using covariates

Innovation

• Adapts LRDD into statistical learning framework
• Integrates exogenous and dynamic covariates
• Forecasts discontinuities and slope changes