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.