Introduction

Causal diagrams have revolutionized the way in which researchers ask: Does X have a causal effect on Y? They have become a key tool for researchers who study the effects of treatments, exposures, and policies. By summarizing and communicating assumptions about the causal structure of a problem, causal diagrams have helped clarify apparent paradoxes, describe common biases, and identify adjustment variables. As a result, a sound understanding of causal diagrams is becoming increasingly important in many scientific disciplines.

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The first part of this course is comprised of five lessons that introduce the theory of causal diagrams and describe its applications to causal inference. The fifth lesson provides a simple graphical description of the bias of conventional statistical methods for confounding adjustment in the presence of time-varying covariates. The second part of the course presents a series of case studies that highlight the practical applications of causal diagrams to real-world questions from the health and social sciences.

What you'll learn:

  • How to translate expert knowledge into a causal diagram
  • How to draw causal diagrams under different assumptions
  • Using causal diagrams to identify common biases
  • Using causal diagrams to guide data analysis
Professor Photo Credit: Anders Ahlbom

Meet The Faculty

Miguel Hernán

Miguel Hernán

Kolokotrones Professor of Biostatistics and Epidemiology, Harvard University

Miguel Hernán teaches methods for causal inference at the Harvard Chan School of Public Health, where he is the Kolokotrones Professor of Biostatistics and Epidemiology. As a researcher, he is interested in finding what works in medicine and public health. He has used causal diagrams to help answer questions about HIV, kidney disease, cardiovascular disease, and cancer.
 
He is the author of the upcoming textbook “Causal Inference”, an Editor of Epidemiology, an Associate Editor of the American Journal of Epidemiology and of the Journal of the American Statistical Association, and an elected Fellow of the American Association for the Advancement of Science.

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