research
∙
06/13/2023
An Approach to Nonparametric Inference on the Causal Dose Response Function
The causal dose response curve is commonly selected as the statistical p...
share
research
∙
05/03/2023
Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions
In many fields, including environmental epidemiology, researchers strive...
research
∙
01/27/2023
Multi-task Highly Adaptive Lasso
We propose a novel, fully nonparametric approach for the multi-task lear...
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...
research
∙
11/26/2022
Efficient Targeted Learning of Heterogeneous Treatment Effects for Multiple Subgroups
In biomedical science, analyzing treatment effect heterogeneity plays an...
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...
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...
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...
research
∙
05/03/2022
A Flexible Approach for Predictive Biomarker Discovery
An endeavor central to precision medicine is predictive biomarker discov...
research
∙
04/13/2022
Practical considerations for specifying a super learner
Common tasks encountered in epidemiology, including disease incidence es...
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...
research
∙
10/18/2021
Comparative Methods for the Analysis of Cluster Randomized Trials
Across research disciplines, cluster randomized trials (CRTs) are common...
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...
research
∙
09/21/2021
Personalized Online Machine Learning
In this work, we introduce the Personalized Online Super Learner (POSL) ...
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...
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...
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...
research
∙
01/29/2021
Adaptive Sequential Design for a Single Time-Series
The current work is motivated by the need for robust statistical methods...
research
∙
09/14/2020
Nonparametric causal mediation analysis for stochastic interventional (in)direct effects
Causal mediation analysis has historically been limited in two important...
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 ...
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...
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...
research
∙
06/12/2020
Targeting Learning: Robust Statistics for Reproducible Research
Targeted Learning is a subfield of statistics that unifies advances in c...
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...
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...
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...
research
∙
03/05/2020
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits
We propose the Generalized Policy Elimination (GPE) algorithm, an oracle...
research
∙
12/20/2019
Non-parametric efficient causal mediation with intermediate confounders
Interventional effects for mediation analysis were proposed as a solutio...
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...
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...
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 ...
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...
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...
research
∙
03/08/2019
Transporting stochastic direct and indirect effects to new populations
Transported mediation effects may contribute to understanding how and wh...
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...
research
∙
10/29/2018
Complier stochastic direct effects: identification and robust estimation
Mediation analysis is critical to understanding the mechanisms underlyin...
research
∙
10/06/2018
Robust variance estimation and inference for causal effect estimation
We consider a longitudinal data structure consisting of baseline covaria...
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 ...
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...
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 ...
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 ...
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 ...
research
∙
02/26/2018
Selecting optimal subgroups for treatment using many covariates
We consider the problem of selecting the optimal subgroup to treat when ...
research
∙
02/26/2018
One-step Targeted Maximum Likelihood for Time-to-event Outcomes
Current targeted maximum likelihood estimation methods used to analyze t...
research
∙
07/18/2017
On Adaptive Propensity Score Truncation in Causal Inference
The positivity assumption, or the experimental treatment assignment (ETA...
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 ...
research
∙
05/27/2017
Targeted Learning with Daily EHR Data
Electronic health records (EHR) data provide a cost and time-effective o...
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...
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...
research
∙
10/14/2015
