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.

翻译：我们从观测数据出发，研究从一组离散治疗方案中学习最优策略的问题。提出一种能够平衡强规范性表现与可解释性的分段线性神经网络模型，称为规范性ReLU网络（P-ReLU）。分析表明，该模型（i）将输入空间划分为不相交的多面体区域，同一区域内的所有实例接受相同治疗方案；（ii）可转换为等价且具有超平面分裂的规范性决策树，从而保证可解释性。通过仅对架构进行微调即可轻松整合约束条件，我们展示了P-ReLU网络的灵活性。实验验证了P-ReLU相较于竞争基准方法具有更优的规范性精度。最后，利用真实数据集从训练好的P-ReLU中提取可解释的规范性决策树示例，涵盖无约束与有约束两种场景。