no code implementations • 5 Apr 2024 • Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu, YuXuan Li, Mingduo Zhao, Hiroyasu Iso, Mark van der Laan
We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings.
no code implementations • 24 Jul 2023 • Lars van der Laan, Marco Carone, Alex Luedtke, Mark van der Laan
For this reason, practitioners may resort to simpler models based on parametric or semiparametric assumptions.
no code implementations • 22 May 2022 • Alejandro Schuler, Yi Li, Mark van der Laan
Gradient boosting performs exceptionally in most prediction problems and scales well to large datasets.
1 code implementation • 29 Jun 2021 • Laura B. Balzer, Mark van der Laan, James Ayieko, Moses Kamya, Gabriel Chamie, Joshua Schwab, Diane V. Havlir, Maya L. Petersen
First, outcomes are often missing for some individuals within clusters.
no code implementations • NeurIPS 2021 • Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark van der Laan
Empirical risk minimization (ERM) is the workhorse of machine learning, whether for classification and regression or for off-policy policy learning, but its model-agnostic guarantees can fail when we use adaptively collected data, such as the result of running a contextual bandit algorithm.
no code implementations • NeurIPS 2021 • Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark van der Laan
The adaptive nature of the data collected by contextual bandit algorithms, however, makes this difficult: standard estimators are no longer asymptotically normally distributed and classic confidence intervals fail to provide correct coverage.
no code implementations • 12 May 2021 • Yue You, Mark van der Laan, Philip Collender, Qu Cheng, Alan Hubbard, Nicholas P Jewell, Zhiyue Tom Hu, Robin Mejia, Justin Remais
We cover models assuming a single constraint (identification assumption) on the K-dimensional distribution such that the target quantity is identified and the statistical model is unrestricted.
no code implementations • 29 Jan 2021 • Lina Montoya, Jennifer Skeem, Mark van der Laan, Maya Petersen
In this paper, we study the performance of estimators that approximate the true value of: 1) an $a$ $priori$ known dynamic treatment rule 2) the true, unknown optimal dynamic treatment rule (ODTR); 3) an estimated ODTR, a so-called "data-adaptive parameter," whose true value depends on the sample.
Methodology Applications
no code implementations • 29 Jan 2021 • Lina Montoya, Mark van der Laan, Alexander Luedtke, Jennifer Skeem, Jeremy Coyle, Maya Petersen
Additionally, we highlight practical choices when implementing the algorithm, including choice of candidate algorithms, metalearners to combine the candidates, and risk functions to select the best combination of algorithms.
Applications
1 code implementation • 23 May 2019 • Weixin Cai, Mark van der Laan
In order to optimize the finite sample coverage of the nonparametric bootstrap confidence intervals, we propose a selection method for this sectional variation norm that is based on running the nonparametric bootstrap for all values of the sectional variation norm larger than the one selected by cross-validation, and subsequently determining a value at which the width of the resulting confidence intervals reaches a plateau.
Statistics Theory Methodology Statistics Theory
no code implementations • 30 Aug 2017 • Mark van der Laan
It relies on an initial estimator (HAL-MLE) of the nuisance parameters by minimizing the empirical risk over the parameter space under the constraint that sectional variation norm is bounded by a constant, where this constant can be selected with cross-validation.
Statistics Theory Statistics Theory