Integrating Artificial Intelligence into Causal Research in Epidemiology

Recent advances in Artificial Intelligence (AI) present new and not widely recognized opportunities to advance the rigor, scope, efficiency, and impact of epidemiologic research aiming to make causal inferences or causal decisions. We describe recent developments, challenges, and examples for integrating varied AI tools into the steps of Petersen and van der Laan’s causal inference roadmap and causal decision-making tasks. AI tools relevant to causal research in epidemiology include predictive models, unsupervised learning, causal structure learning, causal estimation, and generative models. Opportunities exist to integrate AI at each stage of the causal roadmap. This includes the use of generative models to synthesize scientific literature and identify knowledge gaps; causal structure learning to discover or hypothesize causal structures from data; unsupervised learning from unstructured text to generate quantitative variables for analysis; predictive models to drive clinical or policy interventions; generative or causal models to assess or establish identifiability; causal models for estimating statistical parameters; and generative models to create text, tables, and figures to interpret and disseminate findings. Researchers must be mindful of potential pitfalls of AI tools such as insufficient training data, poor accuracy, biases, and ethical and legal concerns. Diverse AI tools are available to support causal research in epidemiology. Steps of the causal inference roadmap cannot yet be fully automated, but thoughtful “collaboration” between investigators and AI tools may accelerate or deepen the research at each step.