Explanatory causal effects for model agnostic explanations. (arXiv:2206.11529v1 [cs.LG])

Web: http://arxiv.org/abs/2206.11529

This paper studies the problem of estimating the contributions of features to
the prediction of a specific instance by a machine learning model and the
overall contribution of a feature to the model. The causal effect of a feature
(variable) on the predicted outcome reflects the contribution of the feature to
a prediction very well. A challenge is that most existing causal effects cannot
be estimated from data without a known causal graph. In this paper, we define
an explanatory …

arxiv lg model