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

• URL: http://arxiv.org/abs/2104.12909v6
• Date: Wed, 6 Dec 2023 01:27:41 GMT
• Title: Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules
• Authors: Yusuke Narita and Kohei Yata
• Abstract summary: We develop a treatment-effect estimator using instruments for a class of and deterministic algorithms. We apply our estimator to evaluate the Coronavirus Aid, Relief, and Economic Security Act. The funding is shown to have little effect on COVID-19-related hospital activities.
• Score: 10.134708060109404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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