Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects
arXiv:2303.05032 · doi:10.1002/sim.10153
Abstract
An important strategy for identifying principal causal effects, which are often used 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 illustrate the proposed sensitivity analyses using 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.
References in corpus (4)
- Matching Methods for Causal Inference: A Review and a Look Forward
- Causal Inference Through Potential Outcomes and Principal Stratification: Application to Studies with "Censoring" Due to Death
- An application of principal stratification to control for institutionalization at follow-up in studies of substance abuse treatment programs
- Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects