17 citations · 30 across the 10 of their papers we have counts for
10 papers
Kernel Debiased Plug-in Estimation based on the Universal Least Favorable Submodel
Haiyi Chen, Yang Liu, Ivana Malenica
We propose ULFS-KDPE, a kernel debiased plug-in estimator based on the universal least favorable submodel, for estimating pathwise differentiable parameters in nonparametric models…
Anytime-valid inference in N-of-1 trials
Ivana Malenica, Yongyi Guo, Kyra Gan +1
App-based N-of-1 trials offer a scalable experimental design for assessing the effects of health interventions at an individual level. Their practical success depends on the strong…
Multi-task Highly Adaptive Lasso
Ivana Malenica, Rachael V. Phillips, Daniel Lazzareschi +3
We propose a novel, fully nonparametric approach for the multi-task learning, the Multi-task Highly Adaptive Lasso (MT-HAL). MT-HAL simultaneously learns features, samples and task…
Adaptive Sequential Surveillance with Network and Temporal Dependence
Ivana Malenica, Jeremy R. Coyle, Mark J. van der Laan +1
Strategic test allocation plays a major role in the control of both emerging and existing pandemics (e.g., COVID-19, HIV). Widespread testing supports effective epidemic control by…
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…
Personalized Online Machine Learning
Ivana Malenica, Rachael V. Phillips, Romain Pirracchio +3
In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degre…
Adaptive Sequential Design for a Single Time-Series
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 infere…
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…
More Efficient Off-Policy Evaluation through Regularized Targeted Learning
Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis +1
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…
Robust Estimation of Data-Dependent Causal Effects based on Observing a Single Time-Series
Mark J. van der Laan, Ivana Malenica
Consider the case that one observes a single time-series, where at each time t one observes a data record O(t) involving treatment nodes A(t), possible covariates L(t) and an outco…