Learning Under Moral Hazard with Instrumental Regression and Generalized Method of Moments
For economists and policymakers designing contracts in principal-agent settings with hidden actions, this provides a method to learn contracts from observational data.
The paper tackles the problem of learning optimal contracts under moral hazard when agent actions are unobserved, using instrumental regression and GMM to estimate contracts, and characterizes the shape of the optimal contract.
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.