# A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores

arXiv: https://arxiv.org/abs/2004.04464 · HTML: https://mlsanity.com/paper/2004.04464/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [On Using the Shapley Value for Anomaly Localization: A Statistical Investigation](https://mlsanity.com/paper/2507.21023/) (2025)
- [Statistical Analysis of using the Shapley Value for Sensor Anomaly Localization with Accurate Classifiers](https://mlsanity.com/paper/2606.00867/) (2026)

## Abstract (arXiv)

In anomaly detection, the degree of irregularity is often summarized as a real-valued anomaly score. We address the problem of attributing such anomaly scores to input features for interpreting the results of anomaly detection. We particularly investigate the use of the Shapley value for attributing anomaly scores of semi-supervised detection methods. We propose a characteristic function specifically designed for attributing anomaly scores. The idea is to approximate the absence of some features by locally minimizing the anomaly score with regard to the to-be-absent features. We examine the applicability of the proposed characteristic function and other general approaches for interpreting anomaly scores on multiple datasets and multiple anomaly detection methods. The results indicate the potential utility of the attribution methods including the proposed one.