134 citations · 468 across the 85 of their papers we have counts for
11 papers · 1 filter
Causal Inference When Counterfactuals Depend on the Proportion of All Subjects Exposed
Caleb H. Miles, Maya Petersen, Mark J. van der Laan
The assumption that no subject's exposure affects another subject's outcome, known as the no-interference assumption, has long held a foundational position in the study of causal i…
A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology
Nima S. Hejazi, Philippe Boileau, Mark J. van der Laan +1
The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational biology an…
Uniform Consistency of the Highly Adaptive Lasso Estimator of Infinite Dimensional Parameters
Mark J. van der Laan, Aurélien F. Bibaut
Consider the case that we observe independent and identically distributed copies of a random variable with a probability distribution known to be an element of a specified stat…
Robust and Flexible Estimation of Stochastic Mediation Effects: A Proposed Method and Example in a Randomized Trial Setting
Kara E. Rudolph, Oleg Sofrygin, Wenjing Zheng +1
Causal mediation analysis can improve understanding of the mechanisms underlying epidemiologic associations. However, the utility of natural direct and indirect effect estimation h…
A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure
Laura B. Balzer, Wenjing Zheng, Mark J. van der Laan +1
We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of healt…
Conducting Simulations in Causal Inference with Networks-Based Structural Equation Models
Oleg Sofrygin, Romain Neugebauer, Mark J. van der Laan
The past decade has seen an increasing body of literature devoted to the estimation of causal effects in network-dependent data. However, the validity of many classical statistical…
Causal inference for social network data
Elizabeth L. Ogburn, Oleg Sofrygin, Ivan Diaz +1
We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for depe…
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…
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification
Cheng Ju, Aurélien Bibaut, Mark J. van der Laan
Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation. Howev…
Doubly Robust Inference for Targeted Minimum Loss Based Estimation in Randomized Trials with Missing Outcome Data
Iván Díaz, Mark J. van der Laan
Missing outcome data is one of the principal threats to the validity of treatment effect estimates from randomized trials. The outcome distributions of participants with missing an…
Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods
Cheng Ju, Mary Combs, Samuel D Lendle +4
The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses c…