  This paper discusses capabilities that are essential to models applied in
policy analysis settings and the limitations of direct applications of
off-the-shelf machine learning methodologies to such settings. Traditional
econometric methodologies for building discrete choice models for policy
analysis involve combining data with modeling assumptions guided by
subject-matter considerations. Such considerations are typically most useful in
specifying the systematic component of random utility discrete choice models
but are typically of limited aid in determining the form of the random
component. We identify an area where machine learning paradigms can be
leveraged, namely in specifying and systematically selecting the best
specification of the random component of the utility equations. We review two
recent novel applications where mixed-integer optimization and cross-validation
are used to algorithmically select optimal specifications for the random
utility components of nested logit and logit mixture models subject to
interpretability constraints.
