# XplainAct: Visualization for Personalized Intervention Insights

arXiv: https://arxiv.org/abs/2507.14767 · HTML: https://mlsanity.com/paper/2507.14767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 19 Jul 2025.

## Builds on (most cited first, 15 of 15)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [The State of Applied Econometrics - Causality and Policy Evaluation](https://mlsanity.com/paper/1607.00699/) (2016)
- [A Visual Analytics Approach for Exploratory Causal Analysis: Exploration, Validation, and Applications](https://mlsanity.com/paper/2009.02458/) (2020)
- [Improving Visualization Interpretation Using Counterfactuals](https://mlsanity.com/paper/2107.10309/) (2021)
- [Causalvis: Visualizations for Causal Inference](https://mlsanity.com/paper/2303.00617/) (2023)
- [VAINE: Visualization and AI for Natural Experiments](https://mlsanity.com/paper/2109.04348/) (2021)
- [DOMINO: Visual Causal Reasoning with Time-Dependent Phenomena](https://mlsanity.com/paper/2303.06556/) (2023)
- [Visual Analysis of Multi-outcome Causal Graphs](https://mlsanity.com/paper/2408.02679/) (2024)
- [CausalChat: Interactive Causal Model Development and Refinement Using Large Language Models](https://mlsanity.com/paper/2410.14146/) (2024)
- [CausalPrism: A Visual Analytics Approach for Subgroup-based Causal Heterogeneity Exploration](https://mlsanity.com/paper/2407.01893/) (2024)
- [Beyond Correlation: Incorporating Counterfactual Guidance to Better Support Exploratory Visual Analysis](https://mlsanity.com/paper/2408.16078/) (2024)

## Abstract (arXiv)

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.