6 papers · 1 filter
Sequentially Doubly Robust Estimation of Conditional Survival Probability with Time-Varying Covariates
Hongxiang Qiu, Marco Carone, Alex Luedtke +1
It is often of interest to study the association between covariates and the cumulative incidence of a right-censored time-to-event outcome. When time-varying covariates are measure…
Doubly robust inference via calibration
Lars van der Laan, Alex Luedtke, Marco Carone
Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions. While consistency requires only one of two nui…
Stabilized Inverse Probability Weighting via Isotonic Calibration
Lars van der Laan, Ziming Lin, Marco Carone +1
Inverse weighting with an estimated propensity score is widely used by estimation methods in causal inference to adjust for confounding bias. However, directly inverting propensity…
Debiased machine learning for counterfactual survival functionals based on left-truncated right-censored data
Eric R. Morenz, Charles J. Wolock, Marco Carone
Learning causal effects of a binary exposure on time-to-event endpoints can be challenging because survival times may be partially observed due to censoring and systematically bias…
Propensity Score Augmentation in Matching-based Estimation of Causal Effects
Ernesto Ulloa-Pérez, Marco Carone, Alex Luedtke
When assessing the causal effect of a binary exposure using observational data, confounder imbalance across exposure arms must be addressed. Matching methods, including propensity…
Assessing variable importance in survival analysis using machine learning
Charles J. Wolock, Peter B. Gilbert, Noah Simon +1
Given a collection of features available for inclusion in a predictive model, it may be of interest to quantify the relative importance of a subset of features for the prediction t…