The CAUSALab investigates how to use data to support better decisions in medicine, public health, and policy. Our Center fosters the development of causal inference and AI methodology to learn what works.

Who We Are

Members of the CAUSALab are affiliated to the Departments of Epidemiology and Biostatistics at the Harvard T.H. Chan School of Public Health, and the Departments of Biomedical Informatics and Health Care Policy at the Harvard Medical School. Our collaborators work at top universities, federal agencies, and research organizations.
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What We Do

We generate, analyze, and interpret data so that decision makers—patients, clinicians, regulators, policy makers—can make better decisions. By combining sound methodology and AI with high-quality data, we produce actionable causal inference with real-world impact. We also train the next generation of investigators.
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Our History

In 1986, Professor James Robins described a generalized theory of causal inference from complex longitudinal data with time-varying treatments. This seminal paper marked the beginning of an era in causal inference research from randomized and observational studies. Over the next decades, and under Robins’s scientific guidance, our group in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health made groundbreaking contributions to methodology for causal inference. We applied our methods to address important questions that span a wide range of health areas. In 2021, the CAUSALab was founded 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.

Skin in the Game

Because we generate effect estimates about what works and what harms, our work is often used to support clinical or policy decisions. That is a responsibility that we take quite seriously. If you think we are wrong, help us improve by establishing an adversarial collaboration with the CAUSALab.
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View our recent publications and their associated code.
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We provide software to implement causal inference methods.
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