Sensitivity Analysis for Binary Outcome Misclassification in Randomization Tests via Integer Programming

Conducting a randomization test is a common method for testing causal null hypotheses in randomized experiments. The popularity of randomization tests is largely because their statistical validity only depends on the randomization design, and no distributional or modeling assumption on the outcome v...

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Veröffentlicht in:	Journal of computational and graphical statistics Jg. 34; H. 4; S. 1528 - 1541
Hauptverfasser:	Heng, Siyu, Shaw, Pamela A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Taylor & Francis 02.10.2025 Taylor & Francis Ltd
Schlagworte:	Accuracy Design-based causal inference Fisher's sharp null Integer programming Matched observational studies Neyman's weak null Randomization Randomization inference Sensitivity analysis Warning randomization inference Neyman’s weak null Fisher’s sharp null integer programming matched observational studies design-based causal inference
ISSN:	1061-8600, 1537-2715
Online-Zugang:	Volltext
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