4 citations · 6 across the 5 of their papers we have counts for
13 papers
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…
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…