Yes, conditional and marginal interpretations are different. The drawback to the GEE approach seems to lie primarily in the area of covariance structure selection, using tools such as change in log likelihood or information criteria.  Because the marginal approach uses pseudo-likelihoods, the "data" are not the same under different covariance structures.  In contrast, the conditional approach uses a quasi-likelihood approach, so that information criteria can be used to select a best fitting covariance structure.   Steve Denham ... View more

The EM Reference Help for this node (under SAS Credit Scoring) provides a good amount of detail.  You are correct that the continuous (interval) inputs are first binned via bucket or quantile binning, then those bins are further grouped using either PROC ARBOR or PROC OPTBIN (information about the constrained optimal binning here: http://www2.sas.com/proceedings/forum2008/153-2008.pdf), both using the bins themselves, not the WOE for the bins.   Here is a paragraph from the Reference Help that might be useful:   After the interval variables have been pre-binned, a decision tree model is fitted for each characteristic. PROC ARBOR or PROC OPTBIN (if constrained optimal) is used to produce the groups. You can choose among four grouping methods: optimal criterion, quantile, monotonic event rate, and constrained optimal. The optimal criterion method uses one of two criteria: reduction in entropy measure or the p-value of the Pearson Chi-square statistic. The quantile method generates groups with approximately the same frequency in each group. The monotonic event rate method generates groups that result in a monotonic distribution of event rates across all attributes. The event rate is equal to P(event | attribute). This is the conditional probability of an event given that an applicant exhibits a particular attribute. The constrained optimal method finds an optimal set of groups and simultaneously imposes additional constraints, as specified in the node property panel settings. ... View more