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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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.20782 [cs.LG] |
| (or arXiv:2605.20782v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20782 arXiv-issued DOI via DataCite (pending registration) |
From: Donna Tjandra [view email]
[v1]
Wed, 20 May 2026 06:22:57 UTC (1,008 KB)
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