Skip to content
DraconiticResearch
Search Sources API
203,948 studies31 sourcesUpdated 3 min ago
PreprintCC BY 4.0arXiv

Principal-agent hypothesis testing under adaptive experimentation

  • Ben Chugg
  • Aritra Ghosh
  • Aaditya Ramdas
Open at sourcePDFDOICite

Abstract

Principal-agent hypothesis testing studies statistical contracts in which a principal rewards an agent according to data the agent generates. Existing work focuses on the single-shot setting, where the agent produces a single dataset. We extend the framework to sequential settings, where evidence accumulates over time, rewards may be deferred to the end or disbursed incrementally, and the agent may stop the contract at a strategically chosen stopping time. We call a contract null-deterring if no ''null agent'' (those whose data-generating distribution lies in the null hypothesis) can profit in expectation under any stopping rule, and our main conceptual result shows that such contracts are characterized by e-processes. We then turn to the agent's best response and identify the optimal stopping rule through the Snell envelope of the reward process. We develop simpler, more interpretable approximations that often require the agent to have only partial knowledge of its distribution.

Related

  1. Principal-Agent Hypothesis Testing
    Stephen Bates, Michael I. Jordan, Michael Sklar +1
    Journal of the American Statistical AssociationPeer-reviewedLicence not statedFull text37 citedStatisticsOpenAlex
  2. Truthful and Admissible Principal--Agent Testing with Nuisance Parameters
    Aaditya Ramdas
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  3. Adaptive Experimentation Can Hide Model Error:Detection Limits and Falsification Budget
    Ramnarayan Choudhary
    arXivPreprintCC BY-NC-ND 4.0Full textStatisticsarXiv
  4. Backward SDE characterization of the finite horizon Principal-Agent problem
    Yuxing Huang, Nizar Touzi
    arXivPreprintarXiv licenceFull textOptimisation & controlarXiv
  5. Angular Monotonicity of Completed Dirichlet L-Functions via the Three-Term Zeta Decomposition: A Proof of the Generalized Riemann Hypothesis for Non-Principal Characters
    Zhuo Chen
    Zenodo (CERN European Organization for Nuclear Research)PreprintCC BY5 citedAlgebra & number theoryOpenAlex

Cite

BibTeXRIS
BibTeX
@misc{chugg2026principalagent,
  title = {{Principal-agent hypothesis testing under adaptive experimentation}},
  author = {Ben Chugg and Aritra Ghosh and Aaditya Ramdas},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.38872},
  eprint = {2610.38872},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.38872},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Principal-agent hypothesis testing under adaptive experimentation
AU  - Ben Chugg
AU  - Aritra Ghosh
AU  - Aaditya Ramdas
PY  - 2026
DA  - 2026/10/28
PB  - arXiv
DO  - 10.48550/arxiv.2610.38872
UR  - https://arxiv.org/abs/2610.38872
AB  - Principal-agent hypothesis testing studies statistical contracts in which a principal rewards an agent according to data the agent generates. Existing work focuses on the single-shot setting, where the agent produces a single dataset. We extend the framework to sequential settings, where evidence accumulates over time, rewards may be deferred to the end or disbursed incrementally, and the agent may stop the contract at a strategically chosen stopping time. We call a contract null-deterring if no ''null agent'' (those whose data-generating distribution lies in the null hypothesis) can profit in expectation under any stopping rule, and our main conceptual result shows that such contracts are characterized by e-processes. We then turn to the agent's best response and identify the optimal stopping rule through the Snell envelope of the reward process. We develop simpler, more interpretable approximations that often require the agent to have only partial knowledge of its distribution.
ER  -