Date of this Version
Causal approaches based on the potential outcome framework provide a useful tool for addressing noncompliance problems in randomized trials. We propose a new estimator of causal treatment effects in randomized clinical trials with noncompliance. We use the empirical likelihood approach to construct a profile random sieve likelihood and take into account the mixture structure in outcome distributions, so that our estimator is robust to parametric distribution assumptions and provides substantial finite-sample efficiency gains over the standard instrumental variable estimator. Our estimator is asymptotically equivalent to the standard instrumental variable estimator, and it can be applied to outcome variables with a continuous, ordinal or binary scale. We apply our method to data from a randomized trial of an intervention to improve the treatment of depression among depressed elderly patients in primary care practices.
This is a post-peer-review, pre-copyedit version of an article published in Biometrika.
causal effect, efficient nonparametric estimation, empirical likelihood, instrumental variable, noncompliance, randomized trial
Cheng, J., Small, D., Tan, Z., & Ten Have, T. R. (2009). Efficient Nonparametric Estimation of Causal Effects in Randomized Trials With Noncompliance. Biometrika, 96 (1), 19-36. http://dx.doi.org/10.1093/biomet/asn056
Date Posted: 27 November 2017
This document has been peer reviewed.