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

Yusuke Narita, Kohei Yata

arXiv 26 Apr 2021 · Econometrics · 2 citations (OpenAlex)

arXiv:2104.12909 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

36
references
103
in-text mentions
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distinct cited
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appendix boundary found by appendix_command · 29% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Zajonc, T (2012) Regression Discontinuity Design with Multiple Forcing Variables0.9209378%
2Keele, L. J. and Titiunik, R (2015) Geographic Boundaries as Regression Discontinuities0.8746467%
3Hahn, J., Todd, P. and van der Klaauw, W (2001) Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design0.81142100%
4–- and Wager, S (2019) Optimized Regression Discontinuity Designs0.7375340%
5–- (2021) b)0.7373367%
6–-, –-, –- and Pathak, P. A (2022) Breaking Ties: Regression Discontinuity Design Meets Market Design0.6443267%
7Kakani, P., Chandra, A., Mullainathan, S. and Obermeyer, Z (2020) Allocation of COVID-19 Relief Funding to Disproportionately Black Counties0.6443267%
8–- and Kolesár, M (2021) Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness0.64422100%
9Imbens, G. W. and Angrist, J. D (1994) Identification and Estimation of Local Average Treatment Effects0.64422100%
10Abdulkadiroglu, A., Angrist, J. D., Narita, Y. and Pathak, P. A (2017) Research Design Meets Market Design: Using Centralized Assignment for Impact Evaluation self0.5112250%

Showing the top 10 of 55 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Using Forests in Multivariate Regression Discontinuity Designs0.87482
2Counterfactual Learning with General Data-generating Policies0.73732
3Nonparametric Treatment Effect Identification in School Choice0.64422
4Free Discontinuity Regression With an Application to the Economic Effects of Internet Shutdowns0.40511
5Manipulation Test for Multidimensional RDD0.40511
6Regression Discontinuity Aggregation, with an Application to the Union Effects on Inequality0.40511
7Global Testing in Multivariate Regression Discontinuity Designs0.40511
8Estimating Causal Effects from Data Generated by Stochastic Algorithms0.40511