Stony Brook University Figure 1: The XplainAct interface, illustrated here using the opioid dataset. 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. The advances in machine learning and artificial intelligence in recent years have created a growing need for tools that can effectively support the understanding and modification of complex systems. Traditional analytical methods, which rely on correlation, merely observe how variables tend to change together.