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This paper discusses capabilities that are essential to models applied inpolicy analysis settings and the limitations of direct applications ofoff-the-shelf machine learning methodologies to such settings. Traditionaleconometric methodologies for building discrete choice models for policyanalysis involve combining data with modeling assumptions guided bysubject-matter considerations. Such considerations are typically most useful inspecifying the systematic component of random utility discrete choice modelsbut are typically of limited aid in determining the form of the randomcomponent. We identify an area where machine learning paradigms can beleveraged, namely in specifying and systematically selecting the bestspecification of the random component of the utility equations. We review tworecent novel applications where mixed-integer optimization and cross-validationare used to algorithmically select optimal specifications for the randomutility components of nested logit and logit mixture models subject tointerpretability constraints.

Discrete Choice Analysis with Machine Learning Capabilities