Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health

• URL: http://arxiv.org/abs/2605.20782v1
• Date: Wed, 20 May 2026 06:22:57 GMT
• Title: Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
• Abstract summary: We outline the importance of assessing validity assumptions within available data.<n>Despite advances in causal ML, its limitations remain largely under-appreciated across disciplines.<n>We provide a template to strengthen the rigor and interpretability of causal analyses.
• Score: 34.24946294324514
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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