The CAUSALab investigates how to use data to support better decisions. We foster the development of sound methodology for causal inference and then apply it to answer “what works” questions in medicine, public health, and policy. The CAUSALab resides at the Harvard T.H. Chan School of Public Health.

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 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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Who We Are

The CAUSALab consists of scholars, doctors, researchers, scientists, and consultants.
Meet the CAUSALab Team

Actionable Causal Inference

The real-life impact and application of our work.
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Our History

In 1986, Dr. 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.
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Skin in the Game

We have contributed to develop the language of causal inference and some of its building blocks, but we understand that methodological developments are only the first step to make a difference in the real world. Yes, we talk the talk of causal inference, but we also walk the walk from methods development to real world impact.
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