Efficiency and robustness of causal effect estimators when noncompliance is measured with error
Citations Over TimeTop 23% of 2018 papers
Abstract
Estimating causal effects from randomized controlled trials is often complicated due to participant noncompliance to randomized treatment. Although there are a variety of methods to estimate causal effects in the presence of noncompliance, they generally make the assumption that noncompliance is measured without error. This is frequently an untenable assumption, particularly when noncompliance is based on participant self-report. To overcome this issue, we treat compliance as an unobserved variable and show how to estimate the probability of compliance given a biomarker of treatment and the other observed data. We present inverse probability weighted estimators, regression-based estimators, and a doubly-robust augmented estimator that rely on the estimated probability of compliance rather than an indicator of compliance. We investigate the finite-sample properties of the estimators and their efficiency and robustness under correctly specified or misspecified models, and we apply the estimators to a recently completed trial of very low nicotine content cigarettes.
Related Papers
- → How much should we trust staggered difference-in-differences estimates?(2022)2,725 cited
- → Comparison of causal effect estimators under exposure misclassification(2009)32 cited
- → Mastering Panel Metrics: Causal Impact of Democracy on Growth(2019)8 cited
- → Mastering Panel 'Metrics: Causal Impact of Democracy on Growth(2019)2 cited
- → Model misspecification and bias for inverse probability weighting and doubly robust estimators(2017)