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

Yusuke Narita () and Kohei Yata ()
Additional contact information
Kohei Yata: Yale University

No 2021-022, Working Papers from Human Capital and Economic Opportunity Working Group

Abstract: Algorithms produce a growing portion of decisions and recommendations both in policy and business. Such algorithmic decisions are natural experiments (conditionally quasi-randomly assigned instruments) since the algorithms make decisions based only on observable input variables. We use this observation to develop a treatment-effect estimator for a class of stochastic and deterministic algorithms. Our estimator is shown to be consistent and asymptotically normal for well-defined causal effects. A key special case of our estimator is a high-dimensional regression discontinuity design. The proofs use tools from differential geometry and geometric measure theory, which may be of independent interest. The practical performance of our method is first demonstrated in a high-dimensional simulation resembling decision-making by machine learning algorithms. Our estimator has smaller mean squared errors compared to alternative estimators. We finally apply our estimator to evaluate the effect of Coronavirus Aid, Relief, and Economic Security (CARES) Act, where more than $10 billion worth of relief funding is allocated to hospitals via an algorithmic rule. The estimates suggest that the relief funding has little effects on COVID- 19-related hospital activity levels. Naive OLS and IV estimates exhibit substantial selection bias.

Keywords: natural experiment; treatment effects; geometric measure theory; COVID-19 (search for similar items in EconPapers)
JEL-codes: C90 D70 H51 (search for similar items in EconPapers)
Date: 2021-04
New Economics Papers: this item is included in nep-big and nep-hea
Note: MIP
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Citations: View citations in EconPapers (2)

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http://humcap.uchicago.edu/RePEc/hka/wpaper/Narita ... hm-is-experiment.pdf First version, April 25, 2021 (application/pdf)

Related works:
Working Paper: Algorithm as Experiment: Machine Learning, Market Design, and Policy Eligibility Rules (2023)
Working Paper: Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules (2021)
Working Paper: Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules (2021)
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