most citedCombining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ME2026

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…

stat.ME2026

Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands

Lars van der Laan, Aurelien Bibaut, Nathan Kallus +1

We develop a unified framework for automatic debiased machine learning (autoDML) for inference on a broad class of statistical parameters. The framework applies to any smooth funct…

stat.ML20262 cited

Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts

Lars van der Laan, Marco Carone, Alex Luedtke

We introduce efficient plug-in (EP) learning, a novel framework for the estimation of heterogeneous causal contrasts, such as the conditional average treatment effect and condition…

stat.ME2025

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…

stat.ME2025

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…