181 citations · 241 across the 29 of their papers we have counts for
31 papers
Transforming Conditional Density Estimation Into a Single Nonparametric Regression Task
Alexander G. Reisach, Olivier Collier, Alex Luedtke +1
We propose a way of transforming the problem of conditional density estimation into a single nonparametric regression task via the introduction of auxiliary samples. This allows le…
Data fusion using weakly aligned sources
Sijia Li, Peter B. Gilbert, Rui Duan +1
We introduce a new data fusion method that utilizes multiple data sources to estimate a smooth, finite-dimensional parameter. Most existing methods only make use of fully aligned d…
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…
One-Step Estimation of Differentiable Hilbert-Valued Parameters
Alex Luedtke, Incheoul Chung
We present estimators for smooth Hilbert-valued parameters, where smoothness is characterized by a pathwise differentiability condition. When the parameter space is a reproducing k…
Causal isotonic calibration for heterogeneous treatment effects
Lars van der Laan, Ernesto Ulloa-Pérez, Marco Carone +1
We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a d…
A general adaptive framework for multivariate point null testing
Adam Elder, Marco Carone, Peter Gilbert +1
As a common step in refining their scientific inquiry, investigators are often interested in performing some screening of a collection of given statistical hypotheses. For example,…
Improved Efficiency for Cross-Arm Comparisons via Platform Designs
Tzu-Jung Huang, Alex Luedtke, the AMP Investigators Group
Though platform trials have been touted for their flexibility and streamlined use of trial resources, their statistical efficiency is not well understood. We fill this gap by estab…
Individualized treatment rules under stochastic treatment cost constraints
Hongxiang Qiu, Marco Carone, Alex Luedtke
Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing…
Efficient Estimation of the Maximal Association between Multiple Predictors and a Survival Outcome
Tzu-Jung Huang, Alex Luedtke, Ian W. McKeague
This paper develops a new approach to post-selection inference for screening high-dimensional predictors of survival outcomes. Post-selection inference for right-censored outcome d…
Efficient Estimation Under Data Fusion
Sijia Li, Alex Luedtke
We aim to make inferences about a smooth, finite-dimensional parameter by fusing data from multiple sources together. Previous works have studied the estimation of a variety of par…
Improved inference for vaccine-induced immune responses via shape-constrained methods
Nilanjana Laha, Zoe Moodie, Ying Huang +1
We study the performance of shape-constrained methods for evaluating immune response profiles from early-phase vaccine trials. The motivating problem for this work involves quantif…
Inference for treatment-specific survival curves using machine learning
Ted Westling, Alex Luedtke, Peter Gilbert +1
In the absence of data from a randomized trial, researchers often aim to use observational data to draw causal inference about the effect of a treatment on a time-to-event outcome.…
Estimating the Efficiency Gain of Covariate-Adjusted Analyses in Future Clinical Trials Using External Data
Xiudi Li, Sijia Li, Alex Luedtke
We present a general framework for using existing data to estimate the efficiency gain from using a covariate-adjusted estimator of a marginal treatment effect in a future randomiz…
The Optimal Dynamic Treatment Rule SuperLearner: Considerations, Performance, and Application
Lina Montoya, Mark van der Laan, Alexander Luedtke +3
The optimal dynamic treatment rule (ODTR) framework offers an approach for understanding which kinds of patients respond best to specific treatments -- in other words, treatment ef…
Adversarial Meta-Learning of Gamma-Minimax Estimators That Leverage Prior Knowledge
Hongxiang Qiu, Alex Luedtke
Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution. However, when this knowledge is too…
Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning
Sijia Li, Xiudi Li, Alex Luedtke
We discuss the thought-provoking new objective functions for policy learning that were proposed in "More efficient policy learning via optimal retargeting" by Nathan Kallus and "Le…
Sufficient and insufficient conditions for the stochastic convergence of Cesàro means
Aurélien F. Bibaut, Alex Luedtke, Mark J. van der Laan
We study the stochastic convergence of the Cesàro mean of a sequence of random variables. These arise naturally in statistical problems that have a sequential component, where the…
Universal sieve-based strategies for efficient estimation using machine learning tools
Hongxiang Qiu, Alex Luedtke, Marco Carone
Suppose that we wish to estimate a finite-dimensional summary of one or more function-valued features of an underlying data-generating mechanism under a nonparametric model. One ap…
Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures
Alex Luedtke, Incheoul Chung, Oleg Sofrygin
We frame the meta-learning of prediction procedures as a search for an optimal strategy in a two-player game. In this game, Nature selects a prior over distributions that generate…
Test for Incremental Value of New Biomarkers Based on OR Rules
Lu Wang, Ying Huang, Alexander R Luedtke
In early detection of disease, a single biomarker often has inadequate classification performance, making it important to identify new biomarkers to combine with the existing marke…
Selecting optimal subgroups for treatment using many covariates
Tyler J. VanderWeele, Alex R. Luedtke, Mark J. van der Laan +1
We consider the problem of selecting the optimal subgroup to treat when data on covariates is available from a randomized trial or observational study. We distinguish between four…
Efficient Principally Stratified Treatment Effect Estimation in Crossover Studies with Absorbent Binary Endpoints
Alex Luedtke, Jiacheng Wu
Suppose one wishes to estimate the effect of a binary treatment on a binary endpoint conditional on a post-randomization quantity in a counterfactual world in which all subjects re…
Sequential Double Robustness in Right-Censored Longitudinal Models
Alexander R. Luedtke, Oleg Sofrygin, Mark J. van der Laan +1
Consider estimating the G-formula for the counterfactual mean outcome under a given treatment regime in a longitudinal study. Bang and Robins provided an estimator for this quantit…
Faster Rates for Policy Learning
Alexander Luedtke, Antoine Chambaz
This article improves the existing proven rates of regret decay in optimal policy estimation. We give a margin-free result showing that the regret decay for estimating a within-cla…