可操作的CBFI：结合结构分解与因果反事实追索的表格机器学习方法

中文摘要 AI 辅助

可解释人工智能（XAI）日益要求可操作的因果反事实追索，但现有方法面临因果无效性、认知负担过重和预测失败的挑战。穷尽式因果搜索算法通常需要修改多个属性，而SHAP等基于加性归因的方法忽略高阶特征协同效应，导致XGBoost等复杂非线性模型中预测动力不足、干预精力分散。为弥合这一差距，我们提出可操作的基于案例的特征重要性（A-CBFI），这是一种用于表格机器学习的诊断-处方一体化框架。基于结构因果模型（SCM），A-CBFI分离协同交互瓶颈并释放抑制性结构约束，将其转化为针对性干预措施。通过数学上分离主动用户干预空间（L_active）与下游效应，并将超过98.3%的干预精力集中在已诊断的根本原因上，A-CBFI实现高度针对性的干预。在金融和医疗领域的实证评估表明，A-CBFI将主动人工干预负担降低76.9%，同时保持与穷尽式因果基线相当的全局追索成本。通过优先考虑已诊断的因果瓶颈，A-CBFI提供针对性且可操作的追索，同时保持因果有效性并在所有因果可行实例中实现完全相对收敛。

英文摘要

Explainable artificial intelligence (XAI) increasingly calls for actionable counterfactual recourse, yet current methodologies face challenges related to causal invalidity, excessive cognitive burden, and predictive failure. Exhaustive causal search algorithms often require modifications to multiple attributes, whereas additive attribution-guided methods, such as SHAP, ignore higher-order feature synergies, leading to suboptimal predictive momentum and diffuse intervention effort in complex nonlinear models, such as XGBoost. To bridge this gap, we introduce actionable case-based feature importance (A-CBFI), a diagnosis-prescription integrated framework for tabular machine learning. Grounded in structural causal models (SCMs), A-CBFI isolates synergistic interaction bottlenecks and releases suppressive structural locks, translating them into targeted interventions. By mathematically separating the active user intervention space (L_{\mathrm{active}}) from downstream effects and concentrating over 98.3% of the intervention effort on diagnosed root causes, A-CBFI enables highly targeted interventions. Empirical evaluations across the financial and healthcare domains demonstrate that A-CBFI reduces the active human intervention burden by 77.0% while maintaining comparable global recourse cost to exhaustive causal baselines. By prioritizing the diagnosed causal bottlenecks, A-CBFI provides targeted and actionable recourse while maintaining causal validity and achieving full relative convergence across all causally feasible instances.

发表机构

• Dankook University（檀国大学）

机构由 AI 辅助整理，请以论文原文为准。