XplainAct: Visualization for Personalized Intervention Insights
This addresses the need for personalized intervention insights in fields like epidemiology and social sciences, though it appears incremental as it builds on existing causal methods with a focus on visualization and individual-level analysis.
The authors tackled the problem of existing causal reasoning methods focusing on population-level effects, which fail in heterogeneous systems, by developing XplainAct, a visual analytics framework for simulating and explaining interventions at the individual level within subpopulations, demonstrated through case studies on opioid-related deaths and voting inclinations.
Causality helps people reason about and understand complex systems, particularly through what-if analyses that explore how interventions might alter outcomes. Although existing methods embrace causal reasoning using interventions and counterfactual analysis, they primarily focus on effects at the population level. These approaches often fall short in systems characterized by significant heterogeneity, where the impact of an intervention can vary widely across subgroups. To address this challenge, we present XplainAct, a visual analytics framework that supports simulating, explaining, and reasoning interventions at the individual level within subpopulations. We demonstrate the effectiveness of XplainAct through two case studies: investigating opioid-related deaths in epidemiology and analyzing voting inclinations in the presidential election.