Targeted Learning

Causal Inference for Observational and Experimental Data

Targeted learning is a framework for causal and statistical inference methodology incorporating machine learning. 

The book Targeted Learning: Causal Inference for Observational and Experimental Data, by Mark J. van der Laan and Sherri Rose, was published in 2011. This text focuses largely on cross-sectional studies.

The second book by van der Laan and Rose, Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies, has just been released by Springer in March 2018. This sequel text covers the complicated research questions found in longitudinal and dependent data structures.

Mark J. van der Laan is a Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at the University of California, Berkeley. His research concerns causal inference, prediction, adjusting for missing and censored data, and estimation based on high-dimensional observational and experimental biomedical and genomic data. He is the recipient of the 2005 COPSS Presidents' and Snedecor Awards, as well as the 2004 Spiegelman Award, and is a Founding Editor for the International Journal of Biostatistics.

Sherri Rose is an Associate Professor in the Department of Health Care Policy at Harvard Medical School. Her research interests include semiparametric estimation in causal inference and machine learning for prediction. She co-leads the Health Policy Data Science Lab at Harvard and is a 2017 NIH Director's New Innovator Award recipient. Dr. Rose serves on several editorial boards, including as associate editor for JASA-Theory & Methods and Biostatistics


Both Targeted Learning books are unique in that they also contain wonderful contributions from multiple invited authors, yet are not traditional edited texts. As the authors, we spent significant time crafting and reworking each of the contributed chapters to have consistent style, content, format, and notation as well as a familiar road map. This yields truly cohesive books that each read easily as complete texts.


4/18/16 - Machine Learning and Statistical Inference

1/25/16 - New Paper Posted: "A Generally Efficient Targeted Minimum Loss-Based Estimator"