News & resources

Together with Judea Pearl, Jasjeet Sekhon, and Maya Petersen, I am pleased to announce the launch of the Journal of Causal Inference - a new journal that publishes papers on theoretical and applied causal research across the range of academic disciplines that use quantitative tools to study causality. Our first issue is planned for Fall 2011 and our website is now open for submissions.

Journal of Causal Inference


Together with Alan Hubbard, Michael Jordan, and Rasmus Nielsen, I am leading the NIH-funded Biomedical Big Data Training Program at UC Berkeley. The program responds to the urgent need for advances in data science so that the next generation of scientists has the necessary skills for leveraging the unprecedented and ever-increasing quantity and speed of biomedical information.


I am the PI for the new Berkeley’s Center for Targeted Machine Learning and Causal Inference, which develops, implements and disseminates signature methods for exploiting vast, new health datasets.


New Book: Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies (2017)


Targeted learning methods are critical tools within data science for answering complex statistical questions, including estimands in networks and longitudinal data with time-dependent confounding. We present a scientific roadmap to translate real-world data science applications into formal statistical estimation problems. This is accomplished using the general template of targeted maximum likelihood estimators to construct algorithms that incorporate the state-of-the-art in machine learning for estimation, while still providing valid inference. Standard tools are not currently equipped for these challenges. We include demonstrations with software packages and real data sets, as well as new methodological advances since the publication of the first targeted learning book.


To learn more about Dr. Mark van der Laan and his students, please select the links below:

Read the new book published in 2011 by the Springer Series in Statistics, Targeted Learning: Causal Inference for Observational and Experimental Data.

Read the recent editorial in Amstat News by Mark van der Laan & Sherri Rose: “Statistics Ready for a Revolution”.

Read the recent editorial in STATS.org by Mark van der Laan: “Why We Need a Statistical Revolution”

Statement of Purpose

Research interests

Projects

Collaborators

Students

Papers

UC Berkeley links & collaborators:

Group in Biostatistics, UCB
Department of Statistics, UCB
Genomics Division, UCB & Lawrence Berkeley National Laboratory
Patricia Buffler, PhD, MPH, School of Public Health
Peter Bickel, PhD, Dept. of Statistics
John Colford, MD, PhD, MPH, School of Public Health
Sandrine Dudoit, PhD, Depts. of Biostatistics and Statistics
Alan Hubbard, PhD, Depts. of Biostatistics and Environmental Health Sciences
Nick Jewell, PhD, Depts. of Biostatistics and Statistics
Maya Petersen, MD, PhD, School of Public Health
Bill Satariano, PhD, MPH, School of Public Health
Terry Speed, PhD, Dept. of Statistics
Ira Tager, MD, School of Public Health

Other collaborators:

Adam Arkin, PhD, Lawrence Berkeley National Lab
David Bangsberg, MD, MPH, Harvard University
Antoine Chambaz, PhD, Universite Paris Descartes
Chiron Corporation, a biotechnology company
Mitchell Cohen, UCSF Medical Center
Victor De Grutolla, PhD, Harvard University
Steve Deeks, MD, UCSF
Michael Eisen, PhD, Lawrence Berkeley National Lab
Bruce Fireman, MA, Kaiser Permanente
Richard Gill, PhD, Utrecht University (Netherlands)
Alan Go, PhD, Kaiser Permanente
Marshall Joffe, MD, MPH, PhD, University of Pennsylvania Lawrence Livermore National Laboratory
Geoffrey Manley, MD, UCSF Medical Center
Ian McKeague, PhD, Columbia University
Veronica Miller,PhD, George Washington University
Susan Murphy, PhD, University of Michigan
Romain Neugebauer, PhD, Kaiser Permanente
James Robins, PhD, Harvard University
Michael Rosenblum, PhD, Johns Hopkins University
Bob Shafer, MD, Stanford School of Medicine
Dan Scharfstein, ScD, Johns Hopkins University
Greg Soon, FDA
Aad van der Vaart, PhD, Vrije University (Netherlands)
Thamban Valappil, FDA
UCSF Cancer Center

Professional associations & journals:

American Statistical Association
ASA Bay Area Chapter
Biometrics, Journal of the Int’l Biometric Society
Biometrika
Biostatistics

Contact us

To contact the Division of Biostatistics, or the Ph.D. students on this site:

email: biostat@berkeley.edu
tel: 510-642-3241
fax: 510-643-5163

To contact Mark van der Laan:

Mark J. van der Laan
University of California
Division of Biostatistics
School of Public Health
Earl Warren Hall #7360
Berkeley, California 94720-7360
email: laan@stat.berkeley.edu