 In this paper, we study an approach that applies the Shapley value to conditional expectations (CES) of sets of features (cf. 2%DIFDELCMD < ]%%% ) that subsumes several prior approaches within a common framework. We provide the first algorithm for the general version of CES. We show that CES can result in counterintuitive attributions in theory and in practice (we study a diabetes prediction task);
<meaning-changed>  In this paper, we study an approach that applies the Shapley value to conditional expectations (CES) of sets of features (cf. 2%DIFDELCMD < ]%%% ) that subsumes several prior approaches within a common framework. We provide the first algorithm for the general version of CES. We show that CES can result in counterintuitive attributions in theory and in practice (we study a diabetes prediction task);
 In this paper, we study an approach that applies the Shapley value to conditional expectations (CES) of sets of features (cf. 2%DIFDELCMD < ]%%% These give very different results, rendering the uniqueness result meaningless. Furthermore, we find that previously proposed approaches can produce counterintuitive attributions in theory and in practice (we study a diabetes prediction task);