5 citations · 15 across the 15 of their papers we have counts for
19 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…
Causal machine learning for high-dimensional mediation analysis using interventional effects mapped to a target trial
Tong Chen, Stijn Vansteelandt, David Burgner +2
Causal mediation analysis examines causal pathways linking exposures to disease. The estimation of interventional effects, which are mediation estimands that overcome certain ident…
A roadmap for systematic identification and analysis of multiple biases in causal inference
Rushani Wijesuriya, Rachael A. Hughes, John B. Carlin +3
Observational studies examining causal effects rely on unverifiable assumptions, the violation of which can induce multiple biases. Quantitative bias analysis (QBA) methods examine…
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…
Causal machine learning methods and use of cross-fitting in settings with high-dimensional confounding
Susan Ellul, Stijn Vansteelandt, John B. Carlin +1
Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is chall…