Inferring Effects of Major Events through Discontinuity Forecasting of Population Anxiety
This addresses the problem of estimating causal mental health effects of events for public health policy, though it appears incremental as an adaptation of existing econometric methods to a new framework.
The researchers tackled the problem of estimating community-specific mental health effects of local events by adapting Longitudinal Regression Discontinuity Design (LRDD) into a statistical learning framework to forecast discontinuities and slope changes in anxiety scores. They applied this to predict COVID-19 effects on US counties, achieving correlations of r=+.46 for discontinuity and r=+.65 for slope, showing strong improvement over traditional static representations.
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.