  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.
