In this section, we analyze the vanilla gradient-based explainers and GNNExplainer [24] under the explanation model framework. The proof that this explanation method falls into the class ofadditive feature attribution methods is quite straight-forward. TheconditionG S indicates thattherealization of G must be consistent with the realization of subgraphS. Thus, GNNExplainer would fail to explain predictions of thosemodels. In Figure 1, we provide an example illustrating the impact of theno-child constraint (3) onto the PGMexplanation. However, the constraint changes the edges in the Bayesian network.

Hierarchical Bayesian methods enable information sharing across regression problems on multiple groups of data.

Below, we proceed to describe our contributions in greater detail. Our framework builds upon the seminal work of Jordan et al. [1998], which introduced the celebrated

Deep neural networks (DNNs) have recently emerged as a powerful paradigm for solving Markovian optimal stopping problems. However, a ready extension of DNN-based methods to non-Markovian settings requires significant state and parameter space expansion, manifesting the curse of dimensionality.