Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference. 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

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Course Auditing

EdX

Harvard University

Miguel Hernán

Statistics & Data Analysis

Sci: Mathematics