Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery
因果 SHAP：通过因果发现实现依赖关系感知的特征归因AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接讨论因果发现、因果强度量化及因果关系与相关关系的区分。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
因果 SHAP：通过因果发现实现依赖关系感知的特征归因
- 研究问题
- 当特征高度相关时，如何区分预测解释中的因果作用与相关关系，减少仅与目标相关的特征被赋予过高重要性的问题？
- 方法与思路
- 作者将 PC 因果发现算法与 IDA 因果强度量化算法结合，把推断的因果关系纳入 SHAP 特征归因框架。
- 材料陈述的贡献
- 摘要称，该框架保留 SHAP 的多项理想性质，并在合成与真实数据实验中降低仅有相关关系的特征的归因得分。
- 阅读关注点
- 应核对因果发现与强度量化所需假设、因果图不确定性的处理，以及具体保留哪些 SHAP 性质和如何验证归因。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未说明识别条件，实验表现也不足以证明发现了真实因果关系。
原文摘要
Explaining machine learning (ML) predictions has become crucial as ML models are increasingly deployed in high-stakes domains such as healthcare. While SHapley Additive exPlanations (SHAP) is widely used for model interpretability, it fails to differentiate between causality and correlation, often misattributing feature importance when features are highly correlated. We propose Causal SHAP, a novel framework that integrates causal relationships into feature attribution while preserving many desirable properties of SHAP. By combining the Peter-Clark (PC) algorithm for causal discovery and the Intervention Calculus when the DAG is Absent (IDA) algorithm for causal strength quantification, our approach addresses the weakness of SHAP. Specifically, Causal SHAP reduces attribution scores for features that are merely correlated with the target, as validated through experiments on both synthetic and real-world datasets. This study contributes to the field of Explainable AI (XAI) by providing a practical framework for causal-aware model explanations. Our approach is particularly valuable in domains such as healthcare, where understanding true causal relationships is critical for informed decision-making.