Causal Machine Learning Is Not a Panacea: A Roadmap for Observational Causal Inference in Health
It provides guidance for clinical experts and ML practitioners to responsibly use causal ML in health research, addressing a gap in understanding its limitations.
The paper presents a roadmap for applying causal machine learning to observational clinical data, emphasizing the need to validate causal assumptions and justify modeling choices to avoid biased results.
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.