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

Yusuke Narita () and Kohei Yata

Papers from arXiv.org

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.

Date: 2021-04, Revised 2023-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa and nep-ecm
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://arxiv.org/pdf/2104.12909 Latest version (application/pdf)

Related works:
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)
Working Paper: Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2104.12909

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().