CausalPrism：一种面向基于子群的因果异质性探索的可视化分析方法
CausalPrism: A Visual Analytics Approach for Subgroup-based Causal Heterogeneity Exploration
AI总结：
本文提出CausalPrism可视化分析方法，将因果子群发现建模为约束多目标优化问题并用遗传算法求解Pareto最优子群，结合交互式可视化系统帮助用户探索和解释异质性处理效应，实验证明其优于现有方法。
摘要：
在因果推断中，从观测数据估计异质性处理效应（Heterogeneous Treatment Effects, HTEs）对于理解不同子群如何对处理作出反应至关重要，其应用广泛，如精准医疗和定向广告。然而，现有关于HTE、子群发现和因果可视化的工作不足以应对两个挑战：首先，潜在子群数量庞大，并且需要平衡多个目标（例如高效应和低方差），这构成了相当大的分析挑战。其次，有效的子群分析必须遵循用户指定的分析目标，并提供带有验证的因果结果。为此，我们提出了一种面向基于子群的因果异质性探索的可视化分析方法。具体而言，我们首先将因果子群发现形式化为一个带约束的多目标优化问题，并采用启发式遗传算法来学习由可解释规则描述的最优子群的Pareto前沿。结合该模型，我们开发了一个原型系统CausalPrism，该系统集成了表格可视化、多属性排序和不确定性图，以支持用户交互式地探索和排序子群并解释处理效应。定量实验验证了所提出的模型能够高效地挖掘因果子群，其性能优于最先进的HTE和子群发现方法，案例研究和专家访谈证明了该系统的有效性和可用性。代码可在 https://osf.io/jaqmf/?view_only=ac9575209945476b955bf829c85196e9 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
