AI RESEARCH
Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
arXiv CS.LG
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ArXi:2605.20782v1 Announce Type: new Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal machine learning (ML) for causal inference in observational data. We present a roadmap for applying causal ML to observational data. Materials and methods: We outline the importance of assessing validity assumptions within available data and applying causal ML responsibly for clinical experts using causal ML and ML practitioners with limited clinical expertise.