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Causal Inference on Missing Exposure via Robust Estimation

Abstract Number:

2651 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Yuliang Shi (1), Yeying Zhu (1), Joel Dubin (1)

Institutions:

(1) University of Waterloo, N/A

Co-Author(s):

Yeying Zhu  
University of Waterloo
Joel Dubin  
University of Waterloo

First Author:

Yuliang Shi  
University of Waterloo

Presenting Author:

Yuliang Shi  
University of Waterloo

Abstract Text:

How to deal with missing data in observational studies is a common concern for causal inference. When the covariates are missing at random (MAR), multiple approaches have been provided to help solve the issue. However, if the exposure is MAR, few approaches are available and careful adjustments on both missingness and confounding issues are required to ensure a consistent estimate of the true causal effect on the response. In this article, a new inverse probability weighting (IPW) estimator based on weighted estimating equations (WEE) is proposed to incorporate weights from both the missingness and PS models, which can reduce the joint effect of extreme weights in finite samples. Additionally, we develop a triple robust (TR) estimator via WEE to further protect against the misspecification of the model. The asymptotic properties of WEE estimators are proved using properties of estimating equations. Based on the simulation studies, WEE methods outperform others including imputation-based approaches in terms of bias and standard error. Finally, an application study is conducted to identify the causal effect of the presence of cardiovascular disease on mortality for COVID-19 patients

Keywords:

Missing exposure|Robust estimation|Weighted estimating equations| Multiple imputation|COVID-19|

Sponsors:

SSC (Statistical Society of Canada)

Tracks:

Miscellaneous

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