Optimal Policy Learning: From Theory to Practice

TL;DR

This paper develops a practical framework for optimal policy learning focusing on threshold-based policies, combining theoretical foundations with an empirical illustration using real data to guide policymakers.

Contribution

It introduces a new practical implementation protocol for optimal policy assignment based on theoretical insights, demonstrated with a real dataset.

Findings

The protocol is straightforward to implement with standard software.

The approach effectively identifies optimal threshold-based policies.

Empirical results validate the practical utility of the method.

Abstract

Following in the footsteps of the literature on empirical welfare maximization, this paper wants to contribute by stressing the policymaker perspective via a practical illustration of an optimal policy assignment problem. More specifically, by focusing on the class of threshold-based policies, we first set up the theoretical underpinnings of the policymaker selection problem, to then offer a practical solution to this problem via an empirical illustration using the popular LaLonde (1986) training program dataset. The paper proposes an implementation protocol for the optimal solution that is straightforward to apply and easy to program with standard statistical software.