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

arXiv: https://arxiv.org/abs/2104.12909 · HTML: https://mlsanity.com/paper/2104.12909/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [Counterfactual Learning with General Data-generating Policies](https://mlsanity.com/paper/2212.01925/) (2022)

## Abstract (arXiv)

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.