3 papers
stat.ME2026
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…
stat.ME2025
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…
stat.ME2024
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…