Efficient SAGE Estimation via Causal Structure Learning

MCML Authors

Gunnar König

Dr.

* Former Member

→ Group Bernd Bischl
 Statistical Learning and Data Science

Abstract

The Shapley Additive Global Importance (SAGE) value is a theoretically appealing interpretability method that fairly attributes global importance to a model’s features. However, its exact calculation requires the computation of the feature’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.

inproceedings LKG23

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AISTATS 2023

26th International Conference on Artificial Intelligence and Statistics. Valencia, Spain, Apr 25-27, 2023.

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Authors

C. Luther • G. König • M. Grosse-Wentrup

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Links

URL

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Research Area

 A1 | Statistical Foundations & Explainability