A1597
Title: Evaluating and improving real-world evidence with targeted learning
Authors: Rachael Phillips - University of California Berkeley (United States) [presenting]
Abstract: Society is drowning in data and the current practice of learning from data is to apply traditional statistical methods that are overly simplistic, arbitrarily chosen, and subject to manipulation. Nonetheless, these methods inform policy and science, affecting our sense of reality and judgements. The aim is to expose this practice and present 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.
