Causal Diagrams: Draw Your Assumptions Before Your Conclusions
Learn to use causal diagrams to draw assumptions before conclusions and understand the effects of treatments, exposures, and policies. This course introduces causal DAGs and SWIGs, and provides case studies to illustrate their practical applications in the health and social sciences. Join Professor Anders Ahlbom and explore the power of causal diagrams. ▼
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Course Feature
Cost:
Free
Provider:
Edx
Certificate:
Paid Certification
Language:
English
Start Date:
14th Jun, 2023
Course Overview
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Updated in [June 30th, 2023]
What skills and knowledge will you acquire during this course?
During this course, you will acquire the skills and knowledge to understand and apply causal diagrams in various scientific disciplines. You will learn about causal Directed Acyclic Graphs (DAGs) and the rules that govern them. Additionally, you will gain an understanding of how causal DAGs can be used to represent common forms of bias and time-varying treatments. The course will also cover the limitations of conventional statistical methods for confounding adjustment and introduce another type of causal diagram called SWIGs. Finally, you will learn how to construct causal diagrams and apply them to real-world questions in the health and social sciences through case studies.
How does this course contribute to professional growth?
This course on Causal Diagrams: Draw Your Assumptions Before Your Conclusions contributes to professional growth by providing researchers with a valuable tool for studying the effects of treatments, exposures, and policies. Causal diagrams help researchers clarify paradoxes, identify biases, and determine adjustment variables by summarizing and communicating assumptions about the causal structure of a problem. As a result, a solid understanding of causal diagrams is becoming increasingly important in various scientific disciplines. The course consists of lessons that introduce causal diagrams and their applications to causal inference, as well as case studies that demonstrate the practical applications of causal diagrams in real-world scenarios.
Is this course suitable for preparing further education?
This course is suitable for preparing further education as it provides a comprehensive understanding of causal diagrams and their applications in various scientific disciplines. The course covers the basics of causal diagrams, including causal DAGs and the rules governing them. It also explores common forms of bias and how causal diagrams can be used to represent them. Additionally, the course delves into time-varying treatments, treatment-confounder feedback, and the limitations of conventional statistical methods for confounding adjustment. The second part of the course includes case studies that demonstrate the practical applications of causal diagrams in the health and social sciences. Overall, this course equips learners with the necessary knowledge and skills to effectively analyze and interpret causal relationships, making it a valuable resource for further education.
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