Efficient Nonparametric Estimation of Causal Effects in Randomized Trials With Noncompliance

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causal effect
efficient nonparametric estimation
empirical likelihood
instrumental variable
noncompliance
randomized trial
Biostatistics
Statistics and Probability
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Cheng, Jing
Small, Dylan
Tan, Zhiqiang
Ten Have, Thomas R
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Abstract

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.

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2009-03-01
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Biometrika
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