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.

通过识别 ICECREAM 对于任何模型中的常见和罕见事件进行基于联合的解释的研究，我们提出一种信息论定量度量来衡量变量联合对目标变量分布的影响，实验结果显示 ICECREAM 在解释性和根本原因分析方面优于其他方法，并在两个任务中取得了令人印象深刻的准确性。