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

Yusuke Narita and Kohei Yata
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Yusuke Narita: Yale University
Kohei Yata: Yale University

No 2391, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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.

Pages: 79 pages
Date: 2024-05
New Economics Papers: this item is included in nep-ain, nep-big and nep-cmp
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