Learning Under Moral Hazard with Instrumental Regression and Generalized Method of Moments

ArXi:2405.20642v3 Announce Type: replace 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.