3 citations · 5 across the 12 of their papers we have counts for
8 papers · 1 filter
Targeted maximum likelihood estimation of vaccine effectiveness and immune correlates in test-negative design studies with missing data
Leah I. B. Andrews, Lars van der Laan, Peter B. Gilbert
The test-negative design (TND) is a resource-efficient observational study design that can assess vaccine effectiveness and exposure-proximal immune correlates of disease. The TND…
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…
Adaptive debiased machine learning using data-driven model selection techniques
Lars van der Laan, Marco Carone, Alex Luedtke +1
Debiased machine learning estimators for smooth functionals in nonparametric models can exhibit substantial variability and instability, often leading practitioners to instead rely…
Nonparametric Instrumental Variable Inference with Many Weak Instruments
Lars van der Laan, Nathan Kallus, Aurélien Bibaut
We study inference on linear functionals in the nonparametric instrumental variable (NPIV) problem with a discretely-valued instrument under a many-weak-instruments asymptotic regi…
Undersmoothed LASSO Models for Propensity Score Weighting and Synthetic Negative Control Exposures for Bias Detection
Richard Wyss, Ben B. Hansen, Georg Hahn +2
The propensity score (PS) is often used to control for large numbers of covariates in high-dimensional healthcare database studies. The least absolute shrinkage and selection opera…
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…