Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
Year: 2026
Type: preprint
Abstract: 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. Observations: Despite advances in causal ML, its limitations remain largely under-appreciated across disciplines. This gap in shared knowledge may impact the validity of findings. Discussion: Causal assumptions must be satisfied and modeling choices justified. Otherwise, these approaches risk producing biased or misleading results, with consequences for clinical research and patient care. Conclusion: Causal ML can be a powerful tool for generating causal hypotheses. We provide a template to strengthen the rigor and interpretability of causal analyses.
Source: arXiv (Cornell University)
Institutions: University of Michigan, Imperial College London, Courant Institute of Mathematical Sciences, New York University, Elon University, University of Cambridge, Cambridge University Hospitals NHS Foundation Trust, Columbia University, Harvard University, Massachusetts Institute of Technology
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W7162009462
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