CausalPrism: A Visual Analytics Approach for Subgroup-based Causal Heterogeneity Exploration

TL;DR

CausalPrism is a visual analytics system that enables interactive exploration and discovery of subgroups with heterogeneous treatment effects from observational data, addressing challenges of subgroup complexity and user-specified analysis goals.

Contribution

The paper introduces a novel multi-objective optimization framework and a visual analytics system for effective causal subgroup discovery and interpretation.

Findings

Outperforms existing HTE and subgroup discovery methods in efficiency.

Provides a user-interactive system for causal heterogeneity exploration.

Validated through experiments, case studies, and expert feedback.

Abstract

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.