3 citations · 5 across the 5 of their papers we have counts for
7 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…
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 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…
The ideal trial: defining causal estimands that balance relevance and feasibility in target trial emulations and actual randomized trials
Margarita Moreno-Betancur, Rushani Wijesuriya, John B. Carlin
Causal inference is the goal of randomized trials and many observational studies. The first step in a formal causal inference framework is to define the causal estimand, and in bot…
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…