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A Crash Course in Causality

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!

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Rating 4.6 based on 48 ratings
Length 6 weeks
Effort 5 weeks of study, 3-5 hours per week
Starts Aug 24 (5 weeks ago)
Cost $49
From University of Pennsylvania via Coursera
Instructor Jason A. Roy, Ph.D.
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

causal inference

This was a terrific introduction to causal inference including basic concepts as well as tests and exercises that reinforced learning.

The course is ok, but not having access to the slides is very annoying One of the best courses in Coursera, Professor with lots of experience in a backpack show how to tackle very complex problem of causal inference.

Content was useful for understanding causal inference in a variety of situations.

I enjoyed the course and learned basics of causal inference.

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

I would love to have a similar full-duration course :D gives thorough basic intro to causal inference Awesome!!!

I wish there were more quizzes (at least another 2 more), testing our knowledge of various formulae for computing IPTW (inverse probability of treatment weights), ITT (intent to treat) and at least one more lab in R Hard to understand Over all, this course is extremely helpful for students who are interested in causal inference of observational data.

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excellent course

Excellent course!

Excellent courses.

nonsense, in depth and practical Excellent course.

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easy to follow

The lectures are very clear and easy to follow, and Professor Roy is really good at explaining the concepts in a simple way.

Very easy to follow examples and great coverage for such an important topic!

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useful for

The course is very useful for beginners.

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Rating 4.6 based on 48 ratings
Length 6 weeks
Effort 5 weeks of study, 3-5 hours per week
Starts Aug 24 (5 weeks ago)
Cost $49
From University of Pennsylvania via Coursera
Instructor Jason A. Roy, Ph.D.
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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