13 citations · 43 across the 8 of their papers we have counts for
8 papers
Robust inference on the average treatment effect using the outcome highly adaptive lasso
Cheng Ju, David Benkeser, Mark J. van der Laan
Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize fl…
Semisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed
Cheng Ju, James Li, Bram Wasti +1
Graph-based semi-supervised learning is a fundamental machine learning problem, and has been well studied. Most studies focus on homogeneous networks (e.g. citation network, friend…
Collaborative targeted inference from continuously indexed nuisance parameter estimators
Cheng Ju, Antoine Chambaz, Mark J. van der Laan
We wish to infer the value of a parameter at a law from which we sample independent observations. The parameter is smooth and we can define two variation-independent features of th…
On Adaptive Propensity Score Truncation in Causal Inference
Cheng Ju, Joshua Schwab, Mark J. van der Laan
The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds,…
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data
Cheng Ju, Richard Wyss, Jessica M. Franklin +3
Propensity score (PS) based estimators are increasingly used for causal inference in observational studies. However, model selection for PS estimation in high-dimensional data has…
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…
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…
Scalable Collaborative Targeted Learning for High-Dimensional Data
Cheng Ju, Susan Gruber, Samuel D. Lendle +5
Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typ…