Counting Defiers: A Design-Based Model of an Experiment Can Reveal Evidence Beyond the Average Effect

Neil Christy and Amanda Kowalski

Papers from arXiv.org

Abstract: Using only a binary intervention and outcome and the design of the randomization within an experiment, we construct a design-based likelihood of the joint distribution of potential outcomes in the sample -- the numbers of always takers, compliers, defiers, and never takers. We develop a visualization to show that samples with defiers can sometimes generate the data in more ways than samples without, yielding a higher likelihood. This likelihood can vary within the Frechet bounds, even though the traditional likelihood does not. Evidence is weak, but it exists, as we illustrate with health applications and our dbmle package.

Date: 2024-12, Revised 2026-03
New Economics Papers: this item is included in nep-ecm, nep-exp, nep-hea and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://arxiv.org/pdf/2412.16352 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2412.16352

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().