  A very popular model-agnostic technique for explaining predictive models is
the SHapley Additive exPlanation (SHAP). The two most popular versions of SHAP
are a conditional expectation version and an unconditional expectation version
(the latter is also known as interventional SHAP). Except for tree-based
methods, usually the unconditional version is used (for computational reasons).
We provide a (surrogate) neural network approach which allows us to efficiently
calculate the conditional version for both neural networks and other regression
models, and which properly considers the dependence structure in the feature
components. This proposal is also useful to provide drop1 and anova analyses in
complex regression models which are similar to their generalized linear model
(GLM) counterparts, and we provide a partial dependence plot (PDP) counterpart
that considers the right dependence structure in the feature components.
