Anomaly Attribution with Likelihood Compensation


  • Tsuyoshi Idé IBM Research, T. J. Watson Research Center
  • Amit Dhurandhar IBM Research
  • Jiří Navrátil IBM Research
  • Moninder Singh IBM Research
  • Naoki Abe IBM Research


Anomaly/Outlier Detection


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.




How to Cite

Idé, T., Dhurandhar, A., Navrátil, J., Singh, M., & Abe, N. (2021). Anomaly Attribution with Likelihood Compensation. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5), 4131-4138. Retrieved from



AAAI Technical Track on Data Mining and Knowledge Management