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

2026-05-20

AI Summary

This work addresses the frequent bias in causal machine learning applications within observational health research, often stemming from overlooked key assumptions. It proposes a clinical-context-oriented roadmap for applying causal machine learning, systematically integrating clinical domain knowledge with causal inference theory to construct an actionable analytical framework. The approach emphasizes evaluating the plausibility of causal assumptions under real-world data constraints and fosters transparent, rigorous, and interpretable causal analyses through close collaboration between clinicians and machine learning researchers. By grounding causal modeling in clinical reality and methodological rigor, the framework enhances both the scientific validity and practical utility of findings derived from observational studies.

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.

Problem

• causal machine learning
• observational data
• causal inference
• clinical research
• validity assumptions

Innovation

• causal machine learning
• observational causal inference
• validity assumptions
• clinical data
• interpretability