Virtual Workshop on Missing Data Challenges in Computation, Statistics and Applications
Causal inference with binary outcomes subject to both missingness and misclassification
Abstract: Causal inference has been widely conducted in various fields and many methods have been proposed for different settings. However, for noisy data with both mismeasurements and missing observations, those methods often break down. In this talk, I will discuss a problem concerning estimation of the average treatment effects (ATE) when binary outcomes are subject to both missingness and misclassification.
The asymptotic biases caused by ignoring missingness and/or misclassification will be examined. Methods of simultaneously correcting for missingness and misclassification effects will be discussed. Simulation studies are conducted to assess the performance of the proposed methods. An application to smoking cessation data is reported to illustrate the use of the proposed methods.