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Algorithms make a growing portion of policy and business decisions. Wedevelop a treatment-effect estimator using algorithmic decisions as instrumentsfor a class of stochastic and deterministic algorithms. Our estimator isconsistent and asymptotically normal for well-defined causal effects. A specialcase of our setup is multidimensional regression discontinuity designs withcomplex boundaries. We apply our estimator to evaluate the Coronavirus Aid,Relief, and Economic Security Act, which allocated many billions of dollarsworth of relief funding to hospitals via an algorithmic rule. The funding isshown to have little effect on COVID-19-related hospital activities. Naiveestimates exhibit selection bias.

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