ArticleStatistics in medicine2024
Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects.
Article in Statistics in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- CACE-MM: using mixed methods to strengthen causal inference in medicine and public health.BMC medical research methodology · 2026Article
- Expanding the Data Science Toolkit: Mixed Methods for Improving Causal Inference.Harvard data science review · 2025Article
- Identification of complier and noncomplier average causal effects in the presence of latent missing-at-random (LMAR) outcomes: a unifying view and choices of assumptions.Biostatistics (Oxford, England) · 2024Article
- Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects.Statistics in medicine · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one-sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome-mean-given-covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI-based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically - for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF-based estimators to be asymptotically normal - with a view to inform nonparametric inference.
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