Pages that link to "Item:Q4556691"

The following pages link to Outcome‐adaptive lasso: Variable selection for causal inference (Q4556691):

Displaying 42 items.

• Regularization and variable selection in Heckman selection model (Q2122823) (← links)
• Sufficient dimension reduction for average causal effect estimation (Q2147407) (← links)
• Outcome-adjusted balance measure for generalized propensity score model selection (Q2156818) (← links)
• High-dimensional causal mediation analysis based on partial linear structural equation models (Q2157518) (← links)
• Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes (Q2163822) (← links)
• A nonparametric super-efficient estimator of the average treatment effect (Q2218085) (← links)
• Comment: Automated analyses: because we can, does it mean we should? (Q2218087) (← links)
• High-dimensional confounding adjustment using continuous Spike and Slab priors (Q2316985) (← links)
• Power comparison for propensity score methods (Q2418070) (← links)
• The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters (Q2684684) (← links)
• Covariate selection with group lasso and doubly robust estimation of causal effects (Q3119797) (← links)
• Automatic variable selection for exposure‐driven propensity score matching with unmeasured confounders (Q3299088) (← links)
• (Q5004046) (← links)
• Variable Selection With Second-Generation <i>P</i>-Values (Q5050808) (← links)
• Data‐adaptive longitudinal model selection in causal inference with collaborative targeted minimum loss‐based estimation (Q5128783) (← links)
• Covariate selection strategies for causal inference: Classification and comparison (Q5208207) (← links)
• Penalized Spline of Propensity Methods for Treatment Comparison: Rejoinder (Q5229888) (← links)
• Quantile treatment effect estimation with dimension reduction (Q5880051) (← links)
• Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties (Q6055532) (← links)
• Joint calibrated estimation of inverse probability of treatment and censoring weights for marginal structural models (Q6055533) (← links)
• Discussion on “Spatial+: a novel approach to spatial confounding” by Emiko Dupont, Simon N. Wood, and Nicole H. Augustin (Q6055671) (← links)
• Variable Selection in Regression-Based Estimation of Dynamic Treatment Regimes (Q6055847) (← links)
• Improving Trial Generalizability Using Observational Studies (Q6055870) (← links)
• Reader reaction to “Outcome‐adaptive lasso: Variable selection for causal inference” by Shortreed and Ertefaie (2017) (Q6056172) (← links)
• Causal effect estimation with censored outcome and covariate selection (Q6067025) (← links)
• Ultra-High Dimensional Variable Selection for Doubly Robust Causal Inference (Q6079782) (← links)
• Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease (Q6110690) (← links)
• Neighborhood-based cross fitting approach to treatment effects with high-dimensional data (Q6170545) (← links)
• Variable selection in double/debiased machine learning for causal inference: an outcome-adaptive approach (Q6204952) (← links)
• Differentially private outcome-weighted learning for optimal dynamic treatment regime estimation (Q6548911) (← links)
• Massive Parallelization of Massive Sample-Size Survival Analysis (Q6552555) (← links)
• Entropy balancing for causal generalization with target sample summary information (Q6589264) (← links)
• Bayesian nonparametric adjustment of confounding (Q6589270) (← links)
• Robust propensity score weighting estimation under missing at random (Q6595778) (← links)
• An inverse probability weighted regression method that accounts for right-censoring for causal inference with multiple treatments and a binary outcome (Q6626894) (← links)
• Balancing vs modeling approaches to weighting in practice (Q6627611) (← links)
• The impact of adjusting for pure predictors of exposure, mediator, and outcome on the variance of natural direct and indirect effect estimators (Q6627780) (← links)
• Confounder selection strategies targeting stable treatment effect estimators (Q6627915) (← links)
• Assessing conditional causal effect via characteristic score (Q6628020) (← links)
• Propensity score weighting for causal subgroup analysis (Q6628460) (← links)
• Bayesian inference for optimal dynamic treatment regimes in practice (Q6636232) (← links)
• Information projection approach to smoothed propensity score weighting for handling selection bias under missing at random (Q6664139) (← links)