Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules

Algorithms make a growing portion of policy and business decisions. The authors develop a treatment-effect estimator using algorithmic decisions as instruments for a class of stochastic and deterministic algorithms. The authors’ estimator is consistent and asymptotically normal for well-defined causal effects. A special case of the authors’ setup is multidimensional regression discontinuity designs with complex boundaries. The authors apply the authors’ estimator to evaluate the Coronavirus Aid, Relief, and Economic Security Act, which allocated many billions of dollars worth of relief funding to hospitals via an algorithmic rule. The funding is shown to have little effect on COVID-19-related hospital activities. Naive estimates exhibit selection bias.