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

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  • Narita, Yusuke
  • Yata, Kohei

Abstract

Algorithms produce a growing portion of decisions and recommendations both in policy and business. Such algorithmic decisions are natural experiments (conditionally quasirandomly 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 decision-making 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 multidimensional regression discontinuity design. We apply our estimator to evaluate the effect of the Coronavirus Aid, Relief, and Economic Security (CARES) Act, where hundreds of billions of dollars worth of relief funding is allocated to hospitals via an algorithmic rule. Our estimates suggest that the relief funding has little effect on COVID- 19-related hospital activity levels. Naive OLS and IV estimates exhibit substantial selection bias.

Suggested Citation

  • Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," CEI Working Paper Series 2021-05, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    • Handle: RePEc:hit:hitcei:2021-05
    Note: December 7, 2021
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    References listed on IDEAS

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    Cited by:

    1. Yusuke Narita & Kyohei Okumura & Akihiro Shimizu & Kohei Yata, 2022. "Counterfactual Learning with General Data-generating Policies," Papers 2212.01925, arXiv.org.

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