Causal Inference with Measurement Error in Outcomes

[TMCSC]

July 09, 2018  10:30-11:30

W303  School of Mathematics, Sichuan University

[lecture]Grace Y. Yi0709.png

SPEAKERS

Grace Y. Yi (University of Waterloo)

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 application, measurement error is ubiquitously present in data collection due to various reasons. Naively ignoring measurement error effects usually yields biased inference results. In this talk, I will discuss the IPW estimation with mismeasured outcome variables. The impact of measurement error for both continuous and discrete outcome variables will be examined. I 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.

ORGANIZERS

Hui Kou (Sichuan University)

Xu Zhang (Sichuan University)

Jie Zhou (Sichuan University)

SUPPORTED BY

Tianyuan Mathematical Center in Southwest China

School of Mathematics, Sichuan University

VIDEO

  • Causal Inference with Measurement Error in Outcomes
  • 10:30 - 11:30, 2018-07-09
  • Grace Y. Yi (University of Waterloo)