5 papers
Highly Adaptive Principal Component Regression
Mingxun Wang, Alejandro Schuler, Mark van der Laan +1
The Highly Adaptive Lasso (HAL) is a nonparametric regression method that achieves almost dimension-free convergence rates under minimal smoothness assumptions, but its implementat…
Causal inference for calibrated scaling interventions on time-to-event processes
Helene Charlotte Wiese Rytgaard, Mark van der Laan
This work develops a flexible inferential framework for nonparametric causal inference in time-to-event settings, based on stochastic interventions defined through multiplicative s…
Regularized Targeted Maximum Likelihood Estimation in Highly Adaptive Lasso Implied Working Models
Yi Li, Sky Qiu, Zeyi Wang +1
We address the challenge of performing Targeted Maximum Likelihood Estimation (TMLE) after an initial Highly Adaptive Lasso (HAL) fit. Existing approaches that utilize the data-ada…
Score-Preserving Targeted Maximum Likelihood Estimation
Noel Pimentel, Alejandro Schuler, Mark van der Laan
Targeted maximum likelihood estimators (TMLEs) are asymptotically optimal among regular, asymptotically linear estimators. In small samples, however, we may be far from "asymptopia…
Highly Adaptive Ridge
Alejandro Schuler, Alexander Hagemeister, Mark van der Laan
In this paper we propose the Highly Adaptive Ridge (HAR): a regression method that achieves a dimension-free L2 convergence rate in the class of right-continuous functio…