Finite Population Identification and Design-Based Sensitivity Analysis

Brendan Kline and Matthew A. Masten

Papers from arXiv.org

Abstract: We develop an approach to sensitivity analysis that uses design distributions to calibrate sensitivity parameters in a finite population model. We use this approach to (1) give a new formal analysis of the role of randomization, (2) provide a new motivation for examining covariate balance, and (3) show how to construct design-based confidence intervals for the average treatment effect, which allow for heterogeneous treatment effects but do not rely on asymptotics. This approach to confidence interval construction relies on partial identification analysis rather than hypothesis test inversion. Moreover, these intervals also have a non-frequentist, identification-based interpretation. We illustrate our approach in three empirical applications.

Date: 2025-04
New Economics Papers: this item is included in nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2504.14127 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:2504.14127

Access Statistics for this paper

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