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CAUSALab uses data to investigate what works in medicine, public health, and policy
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CAUSALab generates, repurposes and analyzes health data so that key decision makers – regulators, clinicians, policy makers and you – can make more informed decisions.
Our research at Harvard T.H. Chan School of Public Health helps improve health outcomes for several key areas. These include infectious disease, cardiovascular disease, cancer, mental health and pregnancy.
We answer questions such as: are diabetes drugs safe during pregnancy? Can HIV therapies be simplified? How effective is a new vaccine? What is the impact of bariatric surgery on cardiovascular disease? What is the comparative effectiveness of treatments for early psychosis?
Actionable Causal Inference
Advanced methods have real world impact when they deliver actionable information. To identify the causal questions that decision makers seek answers to, our team works closely with colleagues embedded in health systems, consults with government agencies and coordinates international consortia of clinical investigators. We then identify research-grade information sources and invest resources building pipelines from the raw data to the analytic datasets. We not only talk the talk of causal inference; we also walk the walk.
Accessible Research
As pioneers in causal inference, we are committed to transparent reporting and resource sharing that empowers investigators at every level. We foster a collaborative environment by developing open-source software for causal inference and freely accessible books & publications. We also train the next generation of investigators in causal inference via comprehensive education programs including online resources, seminar series and in-person courses at Harvard T.H. Chan School of Public Health. These endeavors build capacity in health research and beyond.
Our History
In 1984, Professor James Robins ushered in a new era in causal inference with his landmark paper published in Mathematical Modelling, which described a generalized theory of causal inference from complex longitudinal data with time-varying treatments in both randomized and observational studies.
In the following decades, investigators at the Harvard T.H. Chan School of Public Health made groundbreaking contributions to causal inference methodology under Robins’ scientific guidance. In the 2010s, James Robins and Miguel Hernán released a free textbook, “Causal Inference: What If,” which has become a cornerstone resource for students learning causal methods.
CAUSALab was founded in 2021 under the direction of Miguel Hernán to articulate a growing research portfolio, create synergy with our strategic partners, and provide training on causal inference to the next generation of investigators. Under the CAUSALab umbrella, our investigators continue to conduct cutting-edge research.
In the following decades, investigators at the Harvard T.H. Chan School of Public Health made groundbreaking contributions to causal inference methodology under Robins’ scientific guidance. In the 2010s, James Robins and Miguel Hernán released a free textbook, “Causal Inference: What If,” which has become a cornerstone resource for students learning causal methods.
CAUSALab was founded in 2021 under the direction of Miguel Hernán to articulate a growing research portfolio, create synergy with our strategic partners, and provide training on causal inference to the next generation of investigators. Under the CAUSALab umbrella, our investigators continue to conduct cutting-edge research.
