Targeted learning: The bridge from machine learning to statistical and causal inference
Society is drowning in data and the current practice of learning from data is to apply traditional statistical methods that are too simplistic, arbitrarily chosen, and subject to manipulation. Nonetheless, these methods inform policy and science, affecting our sense of reality and judgements. This talk exposes this deceptive practice, and presents a solution — a principled and reproducible approach, termed targeted learning, for generating actionable and truthful information from complex, real-world data. This approach unifies causal inference, machine learning and deep statistical theory to answer causal questions with statistical confidence.
This is a public lecture, intended for academics from several disciplines and those interested in the role of causal inference in machine learning. The audience will hear about the historical developments that led to the recent "marriage" of causality and machine learning, and then specifically about targeted learning.
About the speaker
Mark Johannes van der Laan is the Jiann-Ping Hsu E. Peace Professor of Biostatistics and Statistics. His research interests include statistical methods in computational biology and causal inference. Mark pioneered the use of machine learning estimation in semiparametric models, in particular, targeted maximum likelihood estimation (TMLE) and developed an ensemble learning algorithm based on cross-validation known as Super Learner.
This session will be live-streamed and recorded – accessible to both internal and external audience