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Learn simple graphical rules that allow you to use intuitive pictures to improve study design and data analysis for causal inference.
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|📚 Field: Design and Data Analysis|
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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
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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.
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