MIBoost extends gradient boosting to multiple imputation by defining a single loss function that produces one set of selected variables across all imputed datasets.
Do machine learning methods lead to similar individualized treat- ment rules? A comparison study on real data
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A contrast-space orthogonal projection framework yields an exact, covariance-aware, study-based decomposition of network meta-analysis estimators into direct and indirect evidence that exactly reconstructs the original estimates.
SIC is a prior-fitted network that amortizes Bayesian survival inference by pretraining on synthetic data generated from a controllable survival prior, delivering competitive or better performance than classical and deep models on real datasets especially in small-sample regimes.
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Complete-case TMLE that includes an outcome-missingness model shows lower bias and greater robustness to positivity violations than multiple imputation approaches, while MI with CART yields lower RMSE and nominal coverage in simulations based on five missingness DAGs and a real epidemiological data.
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MIBoost: A gradient boosting algorithm for variable selection after multiple imputation
MIBoost extends gradient boosting to multiple imputation by defining a single loss function that produces one set of selected variables across all imputed datasets.