What We Do

The CAUSALab uses data to investigate what works in medicine, public health, and policy. We generate, analyze, and interpret data so that decision makers—patients, clinicians, regulators, policy makers…—can make better decisions. By combining sound methodology with high-quality data, we produce actionable causal inference with real-world impact. We also train the next generation of investigators.

Our methodological research focuses on developing a methodological framework for causal inference research based on observational health databases (e.g., administrative claims, electronic health records, biobanks) and pragmatic randomized trials.  Our areas of work include causal inference and AI, transportability, instrumental variable estimation based on genetic variants (aka, Mendelian randomization), g-methods for sustained treatment strategies, and benchmarking of observational studies with randomized trials.

Our applied research focuses on implementing the frameworks to determine comparative effectiveness and safety of health and policy interventions. Our areas of work include infectious diseases, cardiovascular diseases, cancer, mental Health, and pregnancy.