  We emphasize the importance of asking the right question when interpreting
the decisions of a learning model. We discuss a natural extension of the
theoretical machinery from Janzing et. al. 2020, which answers the question
"Why did my model predict a person has cancer?" for answering a more involved
question, "What caused my model to predict a person has cancer?" While the
former quantifies the direct effects of variables on the model, the latter also
accounts for indirect effects, thereby providing meaningful insights wherever
human beings can reason in terms of cause and effect. We propose three broad
categories for interpretations: observational, model-specific and causal each
of which are significant in their own right. Furthermore, this paper quantifies
feature relevance by weaving different natures of interpretations together with
different measures as characteristic functions for Shapley symmetrization.
Besides the widely used expected value of the model, we also discuss measures
of statistical uncertainty and dispersion as informative candidates, and their
merits in generating explanations for each data point, some of which are used
in this context for the first time. These measures are not only useful for
studying the influence of variables on the model output, but also on the
predictive performance of the model, and for that we propose relevant
characteristic functions that are also used for the first time.
