Learning Prescriptive ReLU Networks
Wei Sun Asterios Tsiourvas
We study the problem of learning optimal policy from a set of discrete
treatment options using observational data. We propose a piecewise linear
neural network model that can balance strong prescriptive performance and
interpretability, which we refer to as the prescriptive ReLU network, or
P-ReLU. We show analytically that this model (i) partitions the input space
into disjoint polyhedra, where all instances that belong to the same partition
receive the same treatment, and (ii) can be converted into an equivalent
prescriptive tree with hyperplane splits for interpretability. We demonstrate
the flexibility of the P-ReLU network as constraints can be easily incorporated
with minor modifications to the architecture. Through experiments, we validate
the superior prescriptive accuracy of P-ReLU against competing benchmarks.
Lastly, we present examples of interpretable prescriptive trees extracted from
trained P-ReLUs using a real-world dataset, for both the unconstrained and
constrained scenarios.