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Decomposition of Global Feature Importance Into Direct and Associative Components (DEDACT)

MCML Authors

Gunnar König

Dr.

* Former Member

→ Group Bernd Bischl
Statistical Learning and Data Science

Timo Freiesleben

Dr.

→ Group Tom Sterkenburg
Epistemological Foundations of Machine Learning

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

Giuseppe Casalicchio

Dr.

→ Group Bernd Bischl
Statistical Learning and Data Science

Moritz Grosse-Wentrup

Prof. Dr.

Principal Investigator

* Former Principal Investigator

Abstract

Global model-agnostic feature importance measures either quantify whether features are directly used for a model's predictions (direct importance) or whether they contain prediction-relevant information (associative importance). Direct importance provides causal insight into the model's mechanism, yet it fails to expose the leakage of information from associated but not directly used variables. In contrast, associative importance exposes information leakage but does not provide causal insight into the model's mechanism. We introduce DEDACT - a framework to decompose well-established direct and associative importance measures into their respective associative and direct components. DEDACT provides insight into both the sources of prediction-relevant information in the data and the direct and indirect feature pathways by which the information enters the model. We demonstrate the method's usefulness on simulated examples.

misc