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A doubly robust estimator for the average treatment effect in the context of a mean-reverting measurement error.

ABSTRACT: One of the main limitations of causal inference methods is that they rely on the assumption that all variables are measured without error. A popular approach for handling measurement error is simulation-extrapolation (SIMEX). However, its use for estimating causal effects have been examined only in the context of an additive, non-differential, and homoscedastic classical measurement error structure. In this article we extend the SIMEX methodology, in the context of a mean reverting measurement error structure, to a doubly robust estimator of the average treatment effect when a single covariate is measured with error but the outcome and treatment and treatment indicator are not. Throughout this article we assume that an independent validation sample is available. Simulation studies suggest that our method performs better than a naive approach that simply uses the covariate measured with error.

SUBMITTER: Lenis D

PROVIDER: S-EPMC6415727 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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A doubly robust estimator for the average treatment effect in the context of a mean-reverting measurement error.

Lenis David D Ebnesajjad Cyrus F CF Stuart Elizabeth A EA

Biostatistics (Oxford, England) 20170401 2

One of the main limitations of causal inference methods is that they rely on the assumption that all variables are measured without error. A popular approach for handling measurement error is simulation-extrapolation (SIMEX). However, its use for estimating causal effects have been examined only in the context of an additive, non-differential, and homoscedastic classical measurement error structure. In this article we extend the SIMEX methodology, in the context of a mean reverting measurement e ...[more]

PMID: 27993763

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