Entering the Era of Data Science: Targeted Learning and the Integration of Statistics and Computational Data Analysis

This outlook article will appear in Advances in Statistics and it reviews the research of Dr. van der Laan\u27s group on Targeted Learning, a subfield of statistics that is concerned with the construction of data adaptive estimators of user-supplied target parameters of the probability distribution of the data and corresponding confidence intervals, aiming to only rely on realistic statistical assumptions. Targeted Learning fully utilizes the state of the art in machine learning tools, while still preserving the important identity of statistics as a field that is concerned with both accurate estimation of the true target parameter value and assessment of uncertainty in order to make sound statistical conclusions. We also provide a philosophical historical perspective on Targeted Learning, also relating it to the new developments in Big Data. We conclude with some remarks explaining the immediate relevance of Targeted Learning to the current big data movement

Targeted Learning for Causality and Statistical Analysis in Medical Research

The authors present the use of targeted learning methods for medical research, prepared as a chapter for the upcoming book Statistics: Discovering Your Future Power. The targeted learning framework involves the explicit specification of the data, model, and parameter. The estimators are double robust and efficient, and can incorporate machine learning procedures such as the super learner