Director of the CAUSALab at the Harvard T.H. Chan School of Public Health

Miguel conducts research to learn what works to improve human health. He is the Director of the CAUSALab at the Harvard T.H. Chan School of Public Health, where he and his collaborators design analyses of healthcare databases, epidemiologic studies, and randomized trials. As Kolokotrones Professor of Biostatistics and Epidemiology, he teaches causal inference methodology at the Harvard Chan School and clinical epidemiology at the Harvard-MIT Division of Health Sciences and Technology.

His edX course “Causal Diagrams” and his book “Causal Inference: What If”, co-authored with James Robins, are freely available online and widely used for the training of researchers. Miguel is an elected Fellow of the American Association for the Advancement of Science and of the American Statistical Association, Editor Emeritus of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and the Journal of the American Statistical Association.

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Interview with Miguel Hernán
The best of causal inference

When there is one topic, which is really hot, it’s causal inference. I’ve got in contact with it about 20 years ago when analysing observational studies and when nobody working on clinical trials would consider it. But now – after the introduction of the estimands framework – this becomes part of every statisticians toolkit.

Today, we have one of the world leading experts in this field as a guest – Miguel Hernán. I’m talking with him about:

What is the causal inference?
How can this help generate and analyze data to identify better strategies for the treatment and prevention of both infectious and noninfectious diseases?

In today’s episode, we will be diving deep into this interesting topic and talk specifically about the following:

  • How did you get interested in causal inferences?
  • If we have an observational study with 2 arms and differences in baseline variables, what are the best ways to adjust for these?
  • What does this look like for multiple treatment arms?
  • In longer studies, we happen to see switches between different treatments. How do we compare treatments appropriately in these cases?

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I want to help the community of statisticians, data scientists, programmers and other quantitative scientists to be more influential, innovative, and effective. I believe that as a community we can help our research, our regulatory and payer systems, and ultimately physicians and patients take better decisions based on better evidence.

I work to achieve a future in which everyone can access the right evidence in the right format at the right time to make sound decisions.

When my kids are sick, I want to have good evidence to discuss with the physician about the different therapy choices.

When my mother is sick, I want her to understand the evidence and being able to understand it.

When I get sick, I want to find evidence that I can trust and that helps me to have meaningful discussions with my healthcare professionals.

I want to live in a world, where the media reports correctly about medical evidence and in which society distinguishes between fake evidence and real evidence.

Let’s work together to achieve this.