Causal Inference with Misclassified Confounder and Missing Data in the Surrogates
Date
2019-07-05
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Abstract
The causal inference pertains to statistical analyses that researchers evaluate causal effect based on precisely measured data. In an observational study interest often lies in estimating the causal effects which are more naturally interfered by potential confounding factors. However, some confounding variables may be measured with error or classified into an incorrect group or category. It could occur due to the difficulty of tracking a long-term average quantity, unavoidable recall bias in answering a questionnaire, unwillingness of answering sensitive questions, unaffordability of precise measurements, etc. We first investigate the consequences of naively ignoring the misclassification issue in confounding variables on the estimation of average treatment effect (ATE). We then develop an EM algorithm through the latent variable model for parameter estimation and subsequent removal of the estimation bias of ATE in the absence of validation data set. Moreover, we adapt the proposed method to address the additional complication when some surrogates are only partially observed. Variance estimation of ATE is obtained through bootstrap method. Simulation studies are reported to assess the performances of the proposed methods with both continuous and discrete outcome variables. The estimation methods we examined include outcome regression, G-computation, propensity score (PS) matching, PS stratification, inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW). Lastly, we analyze a breast cancer data to illustrate the proposed methods. Discussion and future work are outlined in the end.
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Keywords
Causal Inference, MIsclassification
Citation
Fan, Z. (2019). Causal Inference with Misclassified Confounder and Missing Data in the Surrogates (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.