TITLE: Targeted Maximum Likelihood Estimation (TMLE) for Machine Learning: A Gentle Introduction
SPEAKER:Professor Mark J. van der Laan, University of California at Berkeley
MODERATOR: Bill Wang
During this half-day tutorial, we will delve into the utility of the roadmap of targeted learning for translating real-world data applications to a mathematical and statistical formulation of the relevant research question of interest. Participants will perform hands-on implementation of state-of-the-art targeted maximum likelihood estimators using the tlverse software ecosystem in the R programming language. Participants will actively learn and apply the core principles of the Targeted Learning methodology, which (1) generalizes machine learning to any estimand of interest; (2) obtains an optimal estimator of the given estimand, grounded in theory; (3) integrates modern ensemble machine learning techniques; and (4) provides formal statistical inference in terms of confidence intervals and testing of specified null hypotheses of interest. It is highly recommended for participants to have an understanding of basic statistical concepts such as confounding, probability distributions, confidence intervals, hypothesis tests, and regression. Advanced knowledge of mathematical statistics may be useful but is not necessary. Familiarity with the R programming language will be essential.
Mark van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley. He has made contributions to survival analysis, semiparametric statistics, multiple testing, and causal inference. He also developed the targeted maximum likelihood methodology and general theory for super-learning. He is a founding editor of the Journal of Causal Inference and International Journal of Biostatistics.
He has authored 4 books on targeted learning, censored data and multiple testing, authored over 300 publications, and graduated 45 Ph.D. students.
He received his Ph.D. from Utrecht University in 1993 with a dissertation titled “Efficient and Inefficient Estimation in Semiparametric Models”. He received the COPSS Presidents’ Award in 2005, the Mortimer Spiegelman Award in 2004, and the van Dantzig Award in 2005.