Causal Diagrams: Draw Your Assumptions Before Your Conclusions

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Causal Diagrams: Draw Your Assumptions Before Your Conclusions

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edX online courses
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be tau...
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

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Sciences & Technology
Business & Management
21575 reviews

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.

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 th...

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.

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

Reviews 10/10 stars
5 Reviews for Causal Diagrams: Draw Your Assumptions Before Your Conclusions

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Frank NK profile image
Frank NK profile image

Frank NK

10/10 starsCompleted
8 months, 4 weeks ago
Excellent lectures. Very clear explanations of causal inference and DAG. Looking forward to seeing the follow-up advanced courses in the near future. Especially for the G-methods that are not covered in details.
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Mohammad Shahbaz profile image
Mohammad Shahbaz profile image

Mohammad Shahbaz

10/10 starsCompleted
10 months, 3 weeks ago
The course and instructor are great. I am very happy for taking this course. The content are very useful and you wil learn a lot from lectures. I can say that every minute of this course have an important point for researchers. Don't doubt to taking the course. Thanks to Prof. Hernan and edx.
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Vitor de Souza profile image
Vitor de Souza profile image

Vitor de Souza

10/10 starsCompleted
10 months, 3 weeks ago
The course was amazing. It was the way they sold it. No math required, just critical thinking. Also, apart from the very well taught lectures, they offered a ton of good quality material, so in the future we can get involved more in the topic.
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Vincenzo Adamo profile image
Vincenzo Adamo profile image

Vincenzo Adamo

10/10 starsCompleted
1 year, 1 month ago
I apologize for my English, but I would like to say that for me the course is excellent: in the lectures material, in the concepts developed during the lessons and in the way they are explained by Professor Hernàn.
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Miquel Porta profile image
Miquel Porta profile image

Miquel Porta

10/10 starsTaking Now
1 year, 2 months ago
After 2 weeks / lessons, I am very happy taking this course. Having a great time. Top grades to content, instructor and provision.
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