The 2022 American Causal Inference Conference (ACIC 2022) will be held on May 23-25, 2022, both online and in Berkeley, CA. Hosted by the Berkeley School of Public Health's Center for Targeted Machine Learning and Causal Inference, ACIC is an interdisciplinary conference designed to bring together researchers, students, and practitioners of causal inference with emphasis on theory, methodology, and application.
BIDS Faculty Affiliate Maya Petersen co-chairs the organizing committee for this event, and BIDS Data Science Fellow Ivana Malenica will co-present a full day workshop session on Targeted Machine Learning of the Causal Effects of Dynamic and Shift Interventions with the tlverse R Packages, on Monday, May 23, 9:00 AM – 4:00 PM, with co-presenters Mark van der Laan, Alan Hubbard, Jeremy Coyle, Nima Hejazi, and Rachael Phillips. View the full workshop schedule.
ACIC pre-conference workshops - May 23, 2022
ACIC conference - May 24, 2022 - May 25, 2022
Call for Paper & Poster Proposals
Abstract submission deadline - March 15, 2022.
Christina Da Silva (email@example.com)
Maya Petersen is Associate Professor of Biostatistics and Chair of the Division of Biostatistics at the UC Berkeley School of Public Health. Dr. Petersen’s methodological research focuses on the development and application of novel causal inference methods to problems in health, with an emphasis on longitudinal data and adaptive treatment strategies (dynamic regimes), machine learning methods, and study design and analytic strategies for impact evaluation. Dr. Petersen’s applied work focuses on developing and evaluating improved HIV prevention and care strategies in resource-limited settings. She serves as MPI (with Dr. Diane Havlir and Dr. Moses Kamya) for the Sustainable East Africa Research in Community Health (SEARCH) consortium, and co-leads (with Profs. Mark van der Lan and Alan Hubbard) the Berkeley SPH Center for Targeted Learning.
Ivana Malenica is a Ph.D. student in the Biostatistics Division working with Mark van der Laan, Antoine Chambaz and Alan Hubbard. She earned her Master’s in Biostatistics and Bachelor’s in Mathematics, and spent a year working as a Freeport-McMoRan research fellow in Data Science and Bioinformatics at the Translational Genomics Research Institute (TGen). Some of her prior work centers around mathematical modeling and Bayesian models for allele specific expression. Very broadly, her research interests span non/semi-parametric theory, probability theory, machine learning, causal inference and high-dimensional statistics. Most of her current work involves complex dependent settings (dependence through time and network) and adaptive sequential designs. She is also interested in model selection criteria, optimal individualized treatment, sensitivity analysis, mediation, online learning and software development (ex: medltmle, tstmle, tstmle01, sl3, cvma, tmle3opttx). Malenica is also one of the founding members of the tlverse software ecosystem, and works as a biostatistician on multiple projects at the Kaiser Permanente Research Division, TGen and the Bill & Melinda Gates Foundation.