厘清特征相关时解释模型的因果分歧，推荐使用边际方法
Causal Analysis of Shapley Values: Conditional vs. Marginal
- 用因果推理分析两类解释方法的隐含假设
- 发现条件法在特征相关时存在根本性缺陷
- 适合关注模型可解释性与因果推断的研究者
Shapley值作为机器学习模型解释的主流工具，其计算方式中常见的条件法与边际法在特征相关时会产生不同结果，并引发不良副作用。本文通过因果分析指出，两种方法差异源于对缺失因果信息的不同假设。我们证明条件法在因果视角下本质上不成立。结合已有研究，结论是应优先采用边际法。该工作澄清了文献中相互矛盾的建议，为模型解释提供了更可靠的理论依据。
原文摘要 · Abstract (English)
Shapley values, a game theoretic concept, has been one of the most popular tools for explaining Machine Learning (ML) models in recent years. Unfortunately, the two most common approaches, conditional and marginal, to calculating Shapley values can lead to different results along with some undesirable side effects when features are correlated. This in turn has led to the situation in the literature where contradictory recommendations regarding choice of an approach are provided by different authors. In this paper we aim to resolve this controversy through the use of causal arguments. We show that the differences arise from the implicit assumptions that are made within each method to deal with missing causal information. We also demonstrate that the conditional approach is fundamentally unsound from a causal perspective. This, together with previous work in [1], leads to the conclusion that the marginal approach should be preferred over the conditional one.
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