Harvard University is offering free online course on Causal Diagrams: Draw Your Assumptions before Your Conclusions. This course is comprised of five lessons that introduce the theory of causal diagrams and describe its applications to causal inference.
In this nine-week course, applicants will learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference. This course will start on September 26, 2017.
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Course At A Glance
Length: 9 weeks
Effort: 2-3 hours pw
Subject: Data Analysis & Statistics
Institution: Harvard University and edx
Certificate Available: Yes, Add a Verified Certificate for $99
Session: Course Starts on September 26, 2017
Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. Harvard faculty are engaged with teaching and research to push the boundaries of human knowledge. The University has twelve degree-granting Schools in addition to the Radcliffe Institute for Advanced Study.
About This Course
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.
Why Take This Course?
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.
- 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
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.
How To Join This Course
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