Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Volume 107, Issue 500, Pages 1638-1652Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/01621459.2012.734171
Keywords
Bounds; Causal inference; Generalized method of moments; Local average treatment effects; Marginal structural models; Noncompliance; Parameter identification; Potential outcomes; Structural mean models; Structural models
Categories
Funding
- UK Economic & Social Research Council [RES-060-23-0011]
- U.K. Medical Research Council [G0601625]
- ESRC [ES/H005331/1, ES/E00234X/1] Funding Source: UKRI
- MRC [G0601625] Funding Source: UKRI
- Economic and Social Research Council [ES/E00234X/1, ES/H005331/1] Funding Source: researchfish
- Medical Research Council [G0601625] Funding Source: researchfish
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Instrumental variables (IVs) can be used to construct estimators of exposure effects on the outcomes of studies affected by nonignorable selection of the exposure. Estimators that fail to adjust for the effects of nonignorable selection will be biased and inconsistent. Such situations commonly arise in observational studies, but are also a problem for randomized experiments affected by nonignorable noncompliance. In this article, we review IV estimators for studies in which the outcome is binary, and consider the links between different approaches developed in the statistics and econometrics literatures. The implicit assumptions made by each method are highlighted and compared within our framework. We illustrate our findings through the reanalysis of a randomized placebo-controlled trial, and highlight important directions for future work in this area.
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