Which set of features was responsible for a certain output of a machine learning model? Which components caused the failure of a cloud computing application? These are just two examples of questions we are addressing in this work by Identifying Coalition-based Explanations for Common and Rare Events in Any Model (ICECREAM). Specifically, we propose an information-theoretic quantitative measure for the influence of a coalition of variables on the distribution of a target variable. This allows us to identify which set of factors is essential to obtain a certain outcome, as opposed to well-established explainability and causal contribution analysis methods which can assign contributions only to individual factors and rank them by their importance. In experiments with synthetic and real-world data, we show that ICECREAM outperforms state-of-the-art methods for explainability and root cause analysis, and achieves impressive accuracy in both tasks.

翻译：机器学习模型的某个输出是由哪些特征集共同导致的？云计算应用的故障是由哪些组件引发的？这两个问题正是本文通过提出"任意模型中常见与罕见事件的基于联盟的解释方法"（ICECREAM）所探讨的。具体而言，我们提出了一种基于信息论的定量测度，用于评估变量联盟对目标变量分布的影响。与仅能为单个因素分配重要性并对其进行排序的成熟可解释性和因果贡献分析方法不同，该方法能够识别出达成特定结果所必需的所有因素集合。在合成数据和真实世界数据的实验中，我们证明ICECREAM在可解释性和根因分析方面均优于现有方法，并在两项任务中均达到了令人瞩目的准确率。