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

@InProceedings{pmlr-v139-singal21a,
  title = 	 {Flow-based Attribution in Graphical Models: A Recursive Shapley Approach},
  author =       {Singal, Raghav and Michailidis, George and Ng, Hoiyi},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {9733--9743},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/singal21a/singal21a.pdf},
  url = 	 {https://proceedings.mlr.press/v139/singal21a.html},
  abstract = 	 {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.}
}