1 citations · 2 across the 4 of their papers we have counts for
5 papers
On the use of auxiliary variables in multiple imputation when estimating the average causal effect with missing data
Jiaxin Zhang, S. Ghazaleh Dashti, John B. Carlin +2
Estimating the average causal effect (ACE) using observational data is a key focus in causal inference for which missing data present an important challenge. Multiple imputation (M…
Sensitivity analysis for multivariable missing data using multiple imputation: a tutorial
Cattram D Nguyen, Katherine J Lee, Ian R White +2
Multiple imputation is a popular method for handling missing data, with fully conditional specification (FCS) being one of the predominant imputation approaches for multivariable m…
Sensitivity analysis methods for outcome missingness using substantive-model-compatible multiple imputation and their application in causal inference
Jiaxin Zhang, S. Ghazaleh Dashti, John B. Carlin +3
When using multiple imputation (MI) for missing data, maintaining compatibility between the imputation model and substantive analysis is important for avoiding bias. For example, s…
Handling multivariable missing data in causal mediation analysis estimating interventional effects
S. Ghazaleh Dashti, Katherine J. Lee, Julie A. Simpson +2
The interventional effects approach to causal mediation analysis is increasingly common in epidemiologic research, given its potential to address policy-relevant questions about hy…
Recoverability and estimation of causal effects under typical multivariable missingness mechanisms
Jiaxin Zhang, S. Ghazaleh Dashti, John B. Carlin +2
In the context of missing data, the identifiability or "recoverability" of the average causal effect (ACE) depends on causal and missingness assumptions. The latter can be depicted…