Efficient SAGE Estimation via Causal Structure Learning
- Author(s)
- Christoph Luther, Gunnar König, Moritz Grosse-Wentrup
- Abstract
The Shapley Additive Global Importance (SAGE) value is a theoretically appealing interpretability method that fairly attributes global importance to a model?s surplus performance contributions over an exponential number of feature sets. This is computationally expensive, particularly because estimating the surplus contributions requires sampling from conditional distributions. Thus, SAGE approximation algorithms only take a fraction of the feature sets into account. We propose d-SAGE, a method that accelerates SAGE approximation. d-SAGE is motivated by the observation that conditional independencies (CIs) between a feature and the model target imply zero surplus contributions, such that their computation can be skipped. To identify CIs, we leverage causal structure learning (CSL) to infer a graph that encodes (conditional) independencies in the data as d-separations. This is computationally more efficient because the expense of the one-time graph inference and the d-separation queries is negligible compared to the expense of surplus contribution evaluations. Empirically we demonstrate that d-SAGE enables the efficient and accurate estimation of SAGE values.
- Organisation(s)
- Research Group Neuroinformatics, Research Network Data Science, Vienna Cognitive Science Hub
- External organisation(s)
- Ludwig-Maximilians-Universität München, Universität Wien, Munich Center for Machine Learning (MCML)
- Journal
- Proceedings of Machine Learning Research (PMLR)
- Volume
- 206
- Pages
- 11650-11670
- No. of pages
- 21
- ISSN
- 2640-3498
- Publication date
- 04-2023
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 102019 Machine learning
- Portal url
- https://ucris.univie.ac.at/portal/en/publications/efficient-sage-estimation-via-causal-structure-learning(47080443-a0e9-4ee9-9666-f8c89e367f05).html