8 citations · 10 across the 4 of their papers we have counts for
4 papers · 1 filter
Evaluating the Robustness of Targeted Maximum Likelihood Estimators via Realistic Simulations in Nutrition Intervention Trials
Haodong Li, Sonali Rosete, Jeremy Coyle +9
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, dou…
Targeting Learning: Robust Statistics for Reproducible Research
Jeremy R. Coyle, Nima S. Hejazi, Ivana Malenica +9
Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions w…
A Fundamental Measure of Treatment Effect Heterogeneity
Jonathan Levy, Mark van der Laan, Alan Hubbard +1
We offer a non-parametric plug-in estimator for an important measure of treatment effect variability and provide minimum conditions under which the estimator is asymptotically effi…
A machine learning-based approach for estimating and testing associations with multivariate outcomes
David Benkeser, Andrew Mertens, Benjamin F. Arnold +5
We propose a method for summarizing the strength of association between a set of variables and a multivariate outcome. Classical summary measures are appropriate when linear relati…