  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.
