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Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules
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Algorithms make a growing portion of policy and business decisions. We develop a treatment-effect estimator using algorithmic decisions as instruments for a class of stochastic and deterministic algorithms. Our estimator is consistent and asymptotically normal for well-defined causal effects. A special case of our setup is multidimensional regression discontinuity designs with complex boundaries. We apply our 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.
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Estimating Causal Effects from Data Generated by Stochastic Algorithms
Logging the features and relative probability of one unexposed item alongside the exposed item identifies causal effects of content features from stochastic algorithms even with unobserved confounders.
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