Targeted Learning: Towards a future informed by real world evidence

Presented by Dr. Mark van der Laan, Professor of Statistics and Biostatistics at University of California, Berkeley. Learning from data to support regulatory decision making has to do with translating a real world data experiment into a statistical estimation problem, providing valid inference and assessing the validity of a causal interpretation of the study finding. Producing real world evidence from real world data confers advantages, but also poses challenges that are poorly addressed by traditional statistical approaches. This talk describes how Targeted Learning can better address these challenges, with applications to causal effect estimation, adaptive study design, and online learning. Targeted Learning continually evolves in response to technological advances that bring novel opportunities and new challenges. This future will be discussed..