# Principal-Agent Hypothesis Testing

arXiv: https://arxiv.org/abs/2205.06812 · HTML: https://mlsanity.com/paper/2205.06812/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 13 May 2022.

## Builds on (most cited first, 7 of 7)

- [Safe Testing](https://mlsanity.com/paper/1906.07801/) (2019)
- [Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems](https://mlsanity.com/paper/1405.2875/) (2014)
- [Simple versus Optimal Contracts](https://mlsanity.com/paper/1808.03713/) (2018)
- [Contracts under Moral Hazard and Adverse Selection](https://mlsanity.com/paper/2010.06742/) (2020)
- [The Complexity of Contracts](https://mlsanity.com/paper/2002.12034/) (2020)
- [Bayesian Agency: Linear versus Tractable Contracts](https://mlsanity.com/paper/2106.00319/) (2021)
- [Contracts with Private Cost per Unit-of-Effort](https://mlsanity.com/paper/2111.09179/) (2021)

## Built on by (most cited first, 14 of 14)

- [The Sample Complexity of Online Contract Design](https://mlsanity.com/paper/2211.05732/) (2022)
- [Delegated Classification](https://mlsanity.com/paper/2306.11475/) (2023)
- [Incentivizing Quality Text Generation via Statistical Contracts](https://mlsanity.com/paper/2406.11118/) (2024)
- [Online Learning in a Creator Economy](https://mlsanity.com/paper/2305.11381/) (2023)
- [Delegating Data Collection in Decentralized Machine Learning](https://mlsanity.com/paper/2309.01837/) (2023)
- [Incentive-Theoretic Bayesian Inference for Collaborative Science](https://mlsanity.com/paper/2307.03748/) (2023)
- [Solving Constrained Variational Inequalities via a First-order Interior Point-based Method](https://mlsanity.com/paper/2206.10575/) (2022)
- [Poisoning Bayesian Inference via Data Deletion and Replication](https://mlsanity.com/paper/2503.04480/) (2025)
- [Instance-Adaptive Hypothesis Tests with Heterogeneous Agents](https://mlsanity.com/paper/2510.21178/) (2025)
- [Operationalizing Counterfactual Metrics: Incentives, Ranking, and Information Asymmetry](https://mlsanity.com/paper/2305.14595/) (2023)
- [Incentivizing High-Quality Human Annotations with Golden Questions](https://mlsanity.com/paper/2505.19134/) (2025)
- [Incentive Aware AI Regulations: A Credal Characterisation](https://mlsanity.com/paper/2603.05175/) (2026)
- [Optimizing Social Utility in Sequential Experiments](https://mlsanity.com/paper/2605.06520/) (2026)
- [Bentkus-type asymptotic e-values](https://mlsanity.com/paper/2606.06332/) (2026)

## Abstract (arXiv)

Consider the relationship between a regulator (the principal) and an experimenter (the agent) such as a pharmaceutical company. The pharmaceutical company wishes to sell a drug for profit, whereas the regulator wishes to allow only efficacious drugs to be marketed. The efficacy of the drug is not known to the regulator, so the pharmaceutical company must run a costly trial to prove efficacy to the regulator. Critically, the statistical protocol used to establish efficacy affects the behavior of a strategic, self-interested agent; a lower standard of statistical evidence incentivizes the agent to run more trials that are less likely to be effective. The interaction between the statistical protocol and the incentives of the pharmaceutical company is crucial for understanding this system and designing protocols with high social utility. In this work, we discuss how the regulator can set up a protocol with payoffs based on statistical evidence. We show how to design protocols that are robust to an agent's strategic actions, and derive the optimal protocol in the presence of strategic entrants.