Online Program

Return to main conference page
Tuesday, September 24
Tue, Sep 24, 4:15 PM - 5:30 PM
Thurgood Marshall North
Real-World Evidence, Regulatory Decisions, and Product Safety and Effectiveness

Real-World Data, Machine Learning, and Causal Inference (300983)

*Jie Chen, Merck Research Laboratories 

Keywords: Real-world data, structural causal models, target learning, reinforcement learning

There has recently been an increasing interest in applying statistical and machine learning algorithms to real-world data (RWD) for causality assessment. One of the major challenges in analyzing RWD is confounding bias that can lead to spurious association between an exposure and an outcome. Although there are several different approaches (such as propensity score matching and stratification) to adjusting for confounding bias in causal inference, this presentation will focus on structural causal model (SCM) based machine learning methodologies including target learning and reinforcement learning and application of these approaches to RWD for causal inference.