@article{Idé_Dhurandhar_Navrátil_Singh_Abe_2021, title={Anomaly Attribution with Likelihood Compensation}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/16535}, DOI={10.1609/aaai.v35i5.16535}, abstractNote={This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption from many sensor measurements, we often have a situation where some observed samples may significantly deviate from their prediction. It may be due to a sub-optimal black-box model, or simply because those samples are outliers. In either case, one would ideally want to compute a responsibility score indicative of the extent to which an input variable is responsible for the anomalous output. In this work, we formalize this task as a statistical inverse problem: Given model deviation from the expected value, infer the responsibility score of each of the input variables. We propose a new method called likelihood compensation (LC), which is founded on the likelihood principle and computes a correction to each input variable. To the best of our knowledge, this is the first principled framework that computes a responsibility score for real valued anomalous model deviations. We apply our approach to a real-world building energy prediction task and confirm its utility based on expert feedback.}, number={5}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Idé, Tsuyoshi and Dhurandhar, Amit and Navrátil, Jiří and Singh, Moninder and Abe, Naoki}, year={2021}, month={May}, pages={4131-4138} }