Machine learning has become increasingly popular in informing data-driven policy-making. Policies influence behavior in individuals or populations, and ideally, through observational signals, policy-makers learn which policies are effective. However, in many settings, individual actions cannot be perfectly observed. This issue, known in economics as moral hazard, poses a significant challenge. In this work, we study the foundational multitasking principal-agent contract design problem and demonstrate how instrumental regression and the generalized method of moments (GMM) estimator can be used to estimate or learn a good contract. As a bonus result, we also give a uniformity characterization of the shape of the optimal contract.

翻译：机器学习在数据驱动政策制定中的应用日益广泛。政策会影响个体或群体的行为，理想情况下，政策制定者通过观测信号可以了解哪些政策有效。然而在许多场景中，个体行为无法被完全观测。这一经济学中被称为道德风险的问题带来了重大挑战。本文研究了基础性的多任务委托代理契约设计问题，论证了如何利用工具变量回归和广义矩估计量来估计或学习最优契约。作为附加成果，我们还给出了最优契约形状的一致性刻画。