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Flow-based Attribution in Graphical Models: A Recursive Shapley Approach

Raghav Singal · George Michailidis · Hoiyi Ng


Keywords: [ Social Aspects of Machine Learning ] [ Similar ] [ Algorithms -> Classification; Algorithms -> Meta-Learning; Algorithms -> Multitask and Transfer Learning; Algorithms ] [ Algorithms ] [ Regression ]


We study the attribution problem in a graphical model, wherein the objective is to quantify how the effect of changes at the source nodes propagates through the graph. We develop a model-agnostic flow-based attribution method, called recursive Shapley value (RSV). RSV generalizes a number of existing node-based methods and uniquely satisfies a set of flow-based axioms. In addition to admitting a natural characterization for linear models and facilitating mediation analysis for non-linear models, RSV satisfies a mix of desirable properties discussed in the recent literature, including implementation invariance, sensitivity, monotonicity, and affine scale invariance.

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