Title: Causal inference with measurement error in outcomes
Authors: Grace Yi - University of Waterloo (Canada) [presenting]
Abstract: Inverse probability weighting (IPW) estimation has been popularly used to consistently estimate the average treatment effect (ATE). Its validity, however, is challenged by the presence of error-prone variables. In applications, measurement error is ubiquitously present in data collection due to various reasons. Naively ignoring measurement error effects usually yields biased inference results. We will discuss the IPW estimation with mismeasured outcome variables. The impact of measurement error for both continuous and discrete outcome variables will be examined. We will describe estimation procedures with the outcome misclassification effects accommodated. Consistency and efficiency will be investigated. Numerical studies will be reported to assess the performance of the proposed methods.