This outlook article will appear in Advances in Statistics and it reviews the research of Dr. van der Laan's 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.
van der Laan, Mark J. and Starmans, Richard J.C.M., "Entering the Era of Data Science: Targeted Learning and the Integration of Statistics and Computational Data Analysis" (July 2014). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 327.