Mark J. van der Laan

research

∙ 01/27/2023

Multi-task Highly Adaptive Lasso

We propose a novel, fully nonparametric approach for the multi-task lear...

0 Ivana Malenica, et al. ∙

research

∙ 12/05/2022

Adaptive Sequential Surveillance with Network and Temporal Dependence

Strategic test allocation plays a major role in the control of both emer...

0 Ivana Malenica, et al. ∙

research

∙ 11/26/2022

Efficient Targeted Learning of Heterogeneous Treatment Effects for Multiple Subgroups

In biomedical science, analyzing treatment effect heterogeneity plays an...

0 Waverly Wei, et al. ∙

research

∙ 08/17/2022

Revisiting the propensity score's central role: Towards bridging balance and efficiency in the era of causal machine learning

About forty years ago, in a now–seminal contribution, Rosenbaum Rubi...

0 Nima S. Hejazi, et al. ∙

research

∙ 05/17/2022

Targeted learning: Towards a future informed by real-world evidence

The 21st Century Cures Act of 2016 includes a provision for the U.S. Foo...

0 Susan Gruber, et al. ∙

research

∙ 05/11/2022

Efficient estimation of modified treatment policy effects based on the generalized propensity score

Continuous treatments have posed a significant challenge for causal infe...

0 Nima S. Hejazi, et al. ∙

research

∙ 05/03/2022

A Flexible Approach for Predictive Biomarker Discovery

An endeavor central to precision medicine is predictive biomarker discov...

0 Philippe Boileau, et al. ∙

research

∙ 04/13/2022

Practical considerations for specifying a super learner

Common tasks encountered in epidemiology, including disease incidence es...

0 Rachael V. Phillips, et al. ∙

research

∙ 10/23/2021

Why Machine Learning Cannot Ignore Maximum Likelihood Estimation

The growth of machine learning as a field has been accelerating with inc...

39 Mark J. van der Laan, et al. ∙

research

∙ 10/18/2021

Comparative Methods for the Analysis of Cluster Randomized Trials

Across research disciplines, cluster randomized trials (CRTs) are common...

0 Alejandra Benitez, et al. ∙

research

∙ 09/28/2021

Evaluating the Robustness of Targeted Maximum Likelihood Estimators via Realistic Simulations in Nutrition Intervention Trials

Several recently developed methods have the potential to harness machine...

0 Haodong Li, et al. ∙

research

∙ 09/21/2021

Personalized Online Machine Learning

In this work, we introduce the Personalized Online Super Learner (POSL) ...

53 Ivana Malenica, et al. ∙

research

∙ 07/04/2021

One-step TMLE to target cause-specific absolute risks and survival curves

This paper considers one-step targeted maximum likelihood estimation met...

0 Helene C. W. Rytgaard, et al. ∙

research

∙ 05/05/2021

Continuous-time targeted minimum loss-based estimation of intervention-specific mean outcomes

This paper studies the generalization of the targeted minimum loss-based...

0 Helene C. Rytgaard, et al. ∙

research

∙ 02/19/2021

Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions

The covariance matrix plays a fundamental role in many modern explorator...

0 Philippe Boileau, et al. ∙

research

∙ 01/29/2021

Adaptive Sequential Design for a Single Time-Series

The current work is motivated by the need for robust statistical methods...

2 Ivana Malenica, et al. ∙

research

∙ 09/14/2020

Nonparametric causal mediation analysis for stochastic interventional (in)direct effects

Causal mediation analysis has historically been limited in two important...

0 Nima S. Hejazi, et al. ∙

research

∙ 09/13/2020

Sufficient and insufficient conditions for the stochastic convergence of Cesàro means

We study the stochastic convergence of the Cesàro mean of a sequence of ...

0 Aurélien F. Bibaut, et al. ∙

research

∙ 06/15/2020

Targeted Maximum Likelihood Estimation of Community-based Causal Effect of Community-Level Stochastic Interventions

Unlike the commonly used parametric regression models such as mixed mode...

0 Chi Zhang, et al. ∙

research

∙ 06/15/2020

tmleCommunity: A R Package Implementing Target Maximum Likelihood Estimation for Community-level Data

Over the past years, many applications aim to assess the causal effect o...

0 Chi Zhang, et al. ∙

research

∙ 06/12/2020

Targeting Learning: Robust Statistics for Reproducible Research

Targeted Learning is a subfield of statistics that unifies advances in c...

3 Jeremy R. Coyle, et al. ∙

research

∙ 06/05/2020

Rate-adaptive model selection over a collection of black-box contextual bandit algorithms

We consider the model selection task in the stochastic contextual bandit...

5 Aurélien F. Bibaut, et al. ∙

research

∙ 05/22/2020

Nonparametric inverse probability weighted estimators based on the highly adaptive lasso

Inverse probability weighted estimators are the oldest and potentially m...

8 Ashkan Ertefaie, et al. ∙

research

∙ 03/30/2020

Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials

The advent and subsequent widespread availability of preventive vaccines...

0 Nima S. Hejazi, et al. ∙

research

∙ 03/05/2020

Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits

We propose the Generalized Policy Elimination (GPE) algorithm, an oracle...

10 Aurélien F. Bibaut, et al. ∙

research

∙ 12/20/2019

Non-parametric efficient causal mediation with intermediate confounders

Interventional effects for mediation analysis were proposed as a solutio...

0 Iván Díaz, et al. ∙

research

∙ 12/13/2019

More Efficient Off-Policy Evaluation through Regularized Targeted Learning

We study the problem of off-policy evaluation (OPE) in Reinforcement Lea...

24 Aurélien F. Bibaut, et al. ∙

research

∙ 08/14/2019

Efficient Estimation of Pathwise Differentiable Target Parameters with the Undersmoothed Highly Adaptive Lasso

We consider estimation of a functional parameter of a realistically mode...

0 Mark J. van der Laan, et al. ∙

research

∙ 07/22/2019

Fast rates for empirical risk minimization over càdlàg functions with bounded sectional variation norm

Empirical risk minimization over classes functions that are bounded for ...

0 Aurélien F. Bibaut, et al. ∙

research

∙ 07/22/2019

Fast rates for empirical risk minimization with cadlag losses with bounded sectional variation norm

Empirical risk minimization over sieves of the class F of cadlag functio...

0 Aurélien F. Bibaut, et al. ∙

research

∙ 05/31/2019

Targeted Estimation of L2 Distance Between Densities and its Application to Geo-spatial Data

We examine the integrated squared difference, also known as the L2 dista...

0 George Shan, et al. ∙

research

∙ 03/08/2019

Transporting stochastic direct and indirect effects to new populations

Transported mediation effects may contribute to understanding how and wh...

0 Kara E Rudolph, et al. ∙

research

∙ 01/15/2019

A nonparametric super-efficient estimator of the average treatment effect

Doubly robust estimators of causal effects are a popular means of estima...

0 David Benkeser, et al. ∙

research

∙ 10/29/2018

Complier stochastic direct effects: identification and robust estimation

Mediation analysis is critical to understanding the mechanisms underlyin...

0 Kara E Rudolph, et al. ∙

research

∙ 10/06/2018

Robust variance estimation and inference for causal effect estimation

We consider a longitudinal data structure consisting of baseline covaria...

0 Linh Tran, et al. ∙

research

∙ 09/03/2018

Robust Estimation of Data-Dependent Causal Effects based on Observing a Single Time-Series

Consider the case that one observes a single time-series, where at each ...

0 Mark J. van der Laan, et al. ∙

research

∙ 07/25/2018

Statistical Analysis Plan for SEARCH Phase I: Health Outcomes among Adults

This document provides the analytic plan for evaluating adult HIV incide...

0 Laura B. Balzer, et al. ∙

research

∙ 06/18/2018

Flexible Collaborative Estimation of the Average Causal Effect of a Treatment using the Outcome-Highly-Adaptive Lasso

Many estimators of the average causal effect of an intervention require ...

2 Cheng Ju, et al. ∙

research

∙ 03/31/2018

Collaborative targeted inference from continuously indexed nuisance parameter estimators

We wish to infer the value of a parameter at a law from which we sample ...

0 Cheng Ju, et al. ∙

research

∙ 03/31/2018

Collaborative targeted minimum loss inference from continuously indexed nuisance parameter estimators

Suppose that we wish to infer the value of a statistical parameter at a ...

0 Cheng Ju, et al. ∙

research

∙ 02/26/2018

Selecting optimal subgroups for treatment using many covariates

We consider the problem of selecting the optimal subgroup to treat when ...

0 Tyler J. VanderWeele, et al. ∙

research

∙ 02/26/2018

One-step Targeted Maximum Likelihood for Time-to-event Outcomes

Current targeted maximum likelihood estimation methods used to analyze t...

0 Weixin Cai, et al. ∙

research

∙ 07/18/2017

On Adaptive Propensity Score Truncation in Causal Inference

The positivity assumption, or the experimental treatment assignment (ETA...

0 Cheng Ju, et al. ∙

research

∙ 06/30/2017

Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data

Propensity score (PS) based estimators are increasingly used for causal ...

0 Cheng Ju, et al. ∙

research

∙ 05/27/2017

Targeted Learning with Daily EHR Data

Electronic health records (EHR) data provide a cost and time-effective o...

0 Oleg Sofrygin, et al. ∙

research

∙ 04/05/2017

The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification

Artificial neural networks have been successfully applied to a variety o...

0 Cheng Ju, et al. ∙

research

∙ 03/07/2017

Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods

The optimal learner for prediction modeling varies depending on the unde...

0 Cheng Ju, et al. ∙

research

∙ 10/14/2015

An Omnibus Nonparametric Test of Equality in Distribution for Unknown Functions

We present a novel family of nonparametric omnibus tests of the hypothes...

0 Alexander R. Luedtke, et al. ∙

Mark J. van der Laan

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