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Found 4 papers, 2 papers with code
A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
To enhance the traditional parametric g-formula approach, we developed a more adaptable Bayesian g-formula estimator.
Variable selection with missing data in both covariates and outcomes: Imputation and machine learning
We investigate a general variable selection approach when both the covariates and outcomes can be missing at random and have general missing data patterns.
Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes
The preponderance of large-scale healthcare databases provide abundant opportunities for comparative effectiveness research.
Estimating heterogeneous survival treatment effect in observational data using machine learning
Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes.
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