Beyond Single-Feature Importance with ICECREAM

• URL: http://arxiv.org/abs/2307.09779v1
• Date: Wed, 19 Jul 2023 06:48:33 GMT
• Title: Beyond Single-Feature Importance with ICECREAM
• Abstract summary: We propose an information-theoretic measure for the influence of a coalition of variables on the distribution of a target variable. In experiments with synthetic and real-world data, we show that ICECREAM outperforms state-of-the-art methods for explainability and root cause analysis.
• Score: 0.4970364068620607
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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