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

Course description

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.

<a href="//www.youtube.com/watch?v=Gi7D1NB-8fU" rel="nofollow" target="_blank"><img src="//img.youtube.com/vi/Gi7D1NB-8fU/0.jpg" alt="0" title="How To Choose The Correct Channel Type For Your Video Content " /></a>

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.

Professor Photo Credit: Anders Ahlbom

Faculty

  • Portrait of Miguel Hernán
    Kolokotrones Professor of Biostatistics and Epidemiology, Harvard University

Associated Schools

  • Harvard Faculty of Arts & Sciences

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