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A Treatment-Effect Estimator for Algorithmic Decisions

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

We develop a treatment-effect estimator for a class of stochastic and deterministic algorithms.

The practical performance of our method is first demonstrated in a high-dimensional simulation resembling decision-making by machine learning algorithms.

We finally apply our estimator to evaluate the effect of the 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 littleeffects on covid-19-related hospital activity levels.

Authors

Yusuke Narita, Kohei Yata

Related Topics

Machine learning algorithms

Differential geometry

Geometric measure theory

Treatment effect estimator

Regression discontinuity design

Stochastic and deterministic algorithms

Algorithmic rule

Naive ols and iv estimates

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