no code implementations • 12 Oct 2023 • Arthur Chatton, Michèle Bally, Renée Lévesque, Ivana Malenica, Robert W. Platt, Mireille E. Schnitzer
Obtaining continuously updated predictions is a major challenge for personalised medicine.
no code implementations • 14 Jun 2023 • Brian Cho, Yaroslav Mukhin, Kyra Gan, Ivana Malenica
In the problem of estimating target parameters in nonparametric models with nuisance parameters, substituting the unknown nuisances with nonparametric estimators can introduce "plug-in bias."
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).
no code implementations • 5 Dec 2022 • Ivana Malenica, Jeremy R. Coyle, Mark J. Van Der Laan, Maya L. Petersen
Our causal target parameter is the mean latent outcome we would have obtained after one time-step, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint.
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.
no code implementations • 29 Jan 2021 • Ivana Malenica, Aurelien Bibaut, Mark J. Van Der Laan
The current work is motivated by the need for robust statistical methods for precision medicine; as such, we address the need for statistical methods that provide actionable inference for a single unit at any point in time.
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.
no code implementations • 13 Dec 2019 • Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. Van Der Laan
We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have been generated by a different policy, or policies.