6 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…
Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging
Nicholas Williams, Alejandro Schuler
Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We formalize random seed stabili…
Highly Adaptive Empirical Risk Minimization with Principal Components
Carlos GarcÃa Meixide, Mingxun Wang, Alejandro Schuler +1
The Highly Adaptive Lasso (HAL) delivers unprecedented guarantees in nonparametric minimum loss estimation under minimal smoothness assumptions, such as dimension-free minimax opti…
Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks
Yi Li, David Mccoy, Nolan Gunter +3
Modern deep neural networks are powerful predictive tools yet often lack valid inference for causal parameters, such as treatment effects or entire survival curves. While framework…
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…