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

Understand

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.