Title: Targeted Learning in Data Science: Causal Inference for Observational and Experimental Data
Instructors: Professor Mark J. van der Laan, University of California at Berkeley
Moderator: Bill Wang
This 2-day short course will provide a comprehensive introduction to the field of targeted learning for causal inference and the corresponding tlverse software ecosystem. We will focus on targeted minimum loss-based estimators of causal effects, including those of static, dynamic, optimal dynamic, and stochastic interventions. These multiply robust, efficient plug-in estimators use state-of-the-art, ensemble machine learning tools to flexibly adjust for confounding while yielding valid statistical inference. Estimators will be explored under various real-world scenarios: when the outcome is subject to missingness, when mediators are present on the causal pathway, in high dimensions, under two-phase sampling designs, and in right-censored survival settings possibly subject to competing risks. We will discuss the utility of this robust estimation strategy in comparison to conventional techniques, which often rely on restrictive statistical models and may therefore lead to severely biased inference.
In addition to discussion, this course will incorporate both interactive activities and hands-on, guided R programming exercises, to allow participants the opportunity to familiarise themselves with methodology and tools that will translate to real-world analyses. 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.