May 20, 2026 · cs.LGJ/K move · Enter open · S save
Donna Tjandra, Trenton Chang, Sonali Parbhoo, Rajesh Ranganath+7
Division of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, United States · Department of Electrical and Electronic Engineering, Imperial College London, London, UK · Courant Institute of Mathematical Sciences, New York University, New York, New York, United States · Center for Data Science, New York University, New York, New York, United States · Department of Mathematics & Statistics, Elon University, Elon, North Carolina, United States
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.