Causal AI for Real-World Data

Abstract

Causal AI for Real-World Data provides a compact, rigorous, hands‑on treatment of contemporary causal inference, guiding researchers from question specification and DAG‑based identification to defensible estimation and sensitivity analysis of observational health data. Participants will learn to apply the Causal Roadmap and implement state‑of‑the‑art tools in R and Python — including DAG construction and causal discovery, propensity methods, g‑computation, TMLE with SuperLearner, double machine learning, and VAE‑based generative validation — and leave with reproducible artifacts to support transparent, peer‑reviewable causal claims.