  With the abundance of available data, many enterprises seek to implement
data-driven prescriptive analytics to help them make informed decisions. These
prescriptive policies need to satisfy operational constraints, and proactively
eliminate rule conflicts, both of which are ubiquitous in practice. It is also
desirable for them to be simple and interpretable, so they can be easily
verified and implemented. Existing approaches from the literature center around
constructing variants of prescriptive decision trees to generate interpretable
policies. However, none of the existing methods are able to handle constraints.
In this paper, we propose a scalable method that solves the constrained
prescriptive policy generation problem. We introduce a novel path-based
mixed-integer program (MIP) formulation which identifies a (near) optimal
policy efficiently via column generation. The policy generated can be
represented as a multiway-split tree which is more interpretable and
informative than a binary-split tree due to its shorter rules. We demonstrate
the efficacy of our method with extensive experiments on both synthetic and
real datasets.
