Beyond Single-Feature Importance with ICECREAM
Michael Oesterle Patrick Blöbaum Atalanti A. Mastakouri Elke Kirschbaum
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.