XplainAct: Visualization for Personalized Intervention Insights

TL;DR

XplainAct is a visual analytics framework that enables personalized causal intervention analysis at the individual level within subpopulations, addressing heterogeneity in complex systems.

Contribution

It introduces a novel visualization tool for simulating and explaining individual-level interventions, filling a gap in existing population-focused causal methods.

Findings

Effective in epidemiology case study on opioid deaths

Useful for analyzing voting inclinations in elections

Enhances understanding of heterogeneous intervention effects

Abstract

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.