Volver a A Crash Course in Causality: Inferring Causal Effects from Observational Data

4.7

120 calificaciones

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38 revisiones

We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more!
Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment).
At the end of the course, learners should be able to:
1. Define causal effects using potential outcomes
2. Describe the difference between association and causation
3. Express assumptions with causal graphs
4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting)
5. Identify which causal assumptions are necessary for each type of statistical method
So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!...

por MF

•Dec 28, 2017

I really enjoyed this course, the pace could be more even in parts. Sometimes the pace could be more even and some more books/reference material for further study would be nice.

por FF

•Nov 30, 2017

The material is great. Just wished the professor was more active in the discussion forum. Have not showed up in the forum for weeks. At least there should be a TA or something.

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38 revisiones

por HEF

•Feb 19, 2019

The content is relaxing and easy to understand, yet extremely useful in real life when you are conducting experiments. The well designed quiz each week only takes a little time, but could help you to diagnose problems and remember the key points. I really love this course.

por Christopher Rinaldi

•Feb 11, 2019

I thought this was a good overview and I'm glad I took the course, but I would have preferred more hands on programming assignments.

por Alejandro Alvarez Pérez

•Dec 15, 2018

very good content. Story line is highly concise. However, Lecturer could be more stream-lined the the way of explaining. He sure is a skilled guy, however.

por Michael Noetel

•Dec 09, 2018

Content was useful for understanding causal inference in a variety of situations. Presentation was sometimes slow even on double-speed. Lectures were generally structured from abstract to concrete, which was much harder to follow than if it were presented in english first and then made abstract (Mayer, 2009).

por Mateusz Kobos

•Dec 07, 2018

I enjoyed the course and learned basics of causal inference. What I missed was more exercises with R in order to gain more practical understanding of the material. In particular, it would be great to have exercises where you get some dataset and your task is to calculate given causal effect and you need to come up with an approach and to execute it. This would mimic more closely problems that you encounter in practice.

por Wei Fan

•Nov 25, 2018

This course is quite useful for me to get quick understanding of the causality and causal inference in epidemiologic studies. Thanks to Prof. Roy.

por Manuel Fernandez-Moya

•Oct 21, 2018

Interesting introductory course about causality. Good "compilation" in just 5 weeks.

Thanks!

por Bob Kemp

•Oct 16, 2018

Well taught, easy to follow but potentially very important techniques

por Chris Chang

•Aug 29, 2018

Could use a bit more guidance on the projects, but overall a helpful course. Gets straight to the point.

por clancy birrell

•Aug 29, 2018

no nonsense, in depth and practical

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