Participants will learn to apply the Causal Roadmap and implement state-of-the-art tools in R and Python and leave with reproducible artifacts to support transparent, peer‑reviewable causal claims.
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.
Causal foundation models are pretrained neural networks that estimate causal quantities, such as the average treatment effect, on entirely new datasets using in-context learning without requiring model updates.
Christopher Stith, Hossein Rahmani, Jesse C. Cresswell· 0 citations
A unified causal machine learning framework that integrates structural causal modeling with data-driven estimation techniques to enable robust causal discovery and effect estimation and achieves higher causal discovery accuracy with improved precision and recall of causal edges.
Maria Ulfa, Marwan Alshar'e, Dharmesh Dhabliya et al.· Journal of Data Science· 0 citations
This work introduces Masked Neural Causal Models (NCMs), a provably expressive nonparametric framework for simulating and bounding interventional queries over all models compatible with a given CPDAG given observational data, and gives an optimization objective in the space of masked NCMs that asymptotically recovers t...
Adiba Ejaz, Yu-Shu Pan, Hong Yang et al.· 0 citations
A closed-loop prior selection framework is proposed that casts prior injection as a budget-constrained optimization over a candidate prior pool and shows that the use of LLM causal priors stops being manual trial and error and becomes an empirically verifiable selection problem.
This work proposes expert-guided g-computation, or egg-computation, which combines the complementary strengths of both approaches by connecting the Gantt charts commonly used to map patient trajectories with the causal DAG literature, and develops an LLM-assisted pipeline that reliably scales up expert reasoning.
Patrick Vossler, Jialin Ouyang, F. Guo et al.· 0 citations
