USI - Email

Archive / INF Seminars / INF_2024_03_08_Malenica_Ivana

USI - Email

	Personalized Decision-Making in Highly Dependent Settings

Host: Prof. Ernst-Jan Camiel Wit

Friday

08.03

Online on Microsoft Teams

15:00 - 15:30


	Ivana Malenica Harvard University, United States

Abstract:

Effective management of emerging and existing epidemics requires strategic decisions on where, when, and to whom interventions should be applied. However, personalized decision-making in infectious disease applications introduces new and unique statistical challenges. For instance, the individuals at risk of infection are unknown, the true outcome of interest (positive infection status) is often a latent variable, and the presence of complex dependence reduces data to a single observation. In this work, we investigate an adaptive sequential design under latent outcome structures and unspecified dependence through space and time. The statistical problem is addressed within a nonparametric model that respects the unknown dependence structure. I will begin by formalizing a treatment allocation strategy that utilizes up-to-date data to inform who is at risk of infection in real-time, with favorable theoretical properties. The optimal allocation strategy, or optimal policy, maximizes the mean latent outcome under a resource constraint. The proposed estimator learns the optimal policy over time and exploits the double-robust structure of the efficient influence function of the target parameters of interest. In the second part of the talk, I will present the study of data-adaptive inference on the mean under the optimal policy, where the target parameter adapts over time in response to the observed data (state of the epidemic). Lastly, I present a novel paradigm in nonparametric efficient estimation particularly suited for target parameters with complex dependence.

Biography:

Ivana Malenica is a Wojcicki-Troper Data Science Fellow at the Harvard Data Science Initiative in the Department of Statistics at Harvard University. Previously, she completed her Ph.D. in the Division of Biostatistics at UC Berkeley advised by Professor Mark van der Laan. Before UC Berkeley, she studied mathematics and worked as a fellow at the Translational Genomics Research Institute.
During her graduate studies, she was fortunate to serve as both a BIDS Fellow and the Biomedical Big Data Fellow. She is also a founding core developer of the tlverse project, a software ecosystem dedicated to targeted learning.

Host: Prof. Ernst-Jan Camiel Wit

Click here to join online