In causal inference, estimating Heterogeneous Treatment Effects (HTEs) from observational data is critical for understanding how different subgroups respond to treatments, with broad applications such as precision medicine and targeted advertising. However, existing work on HTE, subgroup discovery, and causal visualization is insufficient to address two challenges: first, the sheer number of potential subgroups and the necessity to balance multiple objectives (e.g., high effects and low variances) pose a considerable analytical challenge. Second, effective subgroup analysis has to follow the analysis goal specified by users and provide causal results with verification. To this end, we propose a visual analytics approach for subgroup-based causal heterogeneity exploration. Specifically, we first formulate causal subgroup discovery as a constrained multi-objective optimization problem and adopt a heuristic genetic algorithm to learn the Pareto front of optimal subgroups described by interpretable rules. Combining with this model, we develop a prototype system, CausalPrism, that incorporates tabular visualization, multi-attribute rankings, and uncertainty plots to support users in interactively exploring and sorting subgroups and explaining treatment effects. Quantitative experiments validate that the proposed model can efficiently mine causal subgroups that outperform state-of-the-art HTE and subgroup discovery methods, and case studies and expert interviews demonstrate the effectiveness and usability of the system. Code is available at https://osf.io/jaqmf/?view_only=ac9575209945476b955bf829c85196e9.

翻译：在因果推断中，从观测数据估计异质性处理效应对于理解不同子群对干预措施的响应至关重要，在精准医学和定向广告等领域具有广泛应用。然而，现有关于HTE估计、子群发现和因果可视化的研究尚不足以应对两大挑战：首先，潜在子群数量庞大，且需平衡多重目标（如高效应值与低方差），构成了显著的分析难题；其次，有效的子群分析必须遵循用户指定的分析目标，并提供经过验证的因果结果。为此，我们提出一种基于子群的因果异质性探索可视化分析方法。具体而言，我们首先将因果子群发现建模为约束多目标优化问题，采用启发式遗传算法学习由可解释规则描述的最优子群帕累托前沿。结合该模型，我们开发了原型系统CausalPrism，该系统通过表格可视化、多属性排序和不确定性图示，支持用户交互式探索与排序子群并解释处理效应。定量实验验证了所提模型能高效挖掘优于当前最优HTE与子群发现方法的因果子群，案例研究与专家访谈证明了系统的有效性和可用性。代码发布于 https://osf.io/jaqmf/?view_only=ac9575209945476b955bf829c85196e9。