Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

no code implementations • 13 Mar 2023 • Matthew J. Smith, Rachael V. Phillips, Miguel Angel Luque-Fernandez, Camille Maringe

We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies.

Causal Inference

Multi-task Highly Adaptive Lasso

no code implementations • 27 Jan 2023 • Ivana Malenica, Rachael V. Phillips, Daniel Lazzareschi, Jeremy R. Coyle, Romain Pirracchio, Mark J. Van Der Laan

We propose a novel, fully nonparametric approach for the multi-task learning, the Multi-task Highly Adaptive Lasso (MT-HAL).

Multi-Task Learning

Personalized Online Machine Learning

no code implementations • 21 Sep 2021 • Ivana Malenica, Rachael V. Phillips, Romain Pirracchio, Antoine Chambaz, Alan Hubbard, Mark J. Van Der Laan

In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degrees of personalization.

BIG-bench Machine Learning Time Series +1

Targeting Learning: Robust Statistics for Reproducible Research

no code implementations • 12 Jun 2020 • Jeremy R. Coyle, Nima S. Hejazi, Ivana Malenica, Rachael V. Phillips, Benjamin F. Arnold, Andrew Mertens, Jade Benjamin-Chung, Weixin Cai, Sonali Dayal, John M. Colford Jr., Alan E. Hubbard, Mark J. Van Der Laan

Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions with statistical confidence.

Causal Inference Survival Analysis