A Crash Course in Causality
Inferring Causal Effects from Observational Data
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Rating | 4.5★ based on 67 ratings |
---|---|
Length | 6 weeks |
Effort | 5 weeks of study, 3-5 hours per week |
Starts | Jun 26 (44 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.
Great intro and overview of the details of Causal Inference methods It's really the easiest way to approach Causality someone who is not from a pure Statistics background.
Better than other courses on causal inference on coursera.
Very practical for beginners in causal inference Great Can not download slides which make the source material very inaccessible A clear and straight-to-the-point introduction to causality.
I work in the field of Marketing, in a company that is actively exploring Causal Inference methods to estimate the impact of ads on the purchase behaviour.
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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!
The examples in R were reasonably easy to follow and reproduce even for someone who has not used R (me).
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clear and easy
The materials are clear and easy to understand.
After reading Pearl's book, Causal Inference in Statistics, I found this course really put some meat on the bones, reviewing the basics and demonstrating, in a very clear and easy to understand way, how to conduct the analyses and make causal inferences.
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observational data
I was familiar with most of the matching methods but learning about other preprocessing methods and approaches really widened my view on how to decide what is the best way to do causal analysis on observational data.
for beginners
The course is very useful for beginners.
useful for
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Rating | 4.5★ based on 67 ratings |
---|---|
Length | 6 weeks |
Effort | 5 weeks of study, 3-5 hours per week |
Starts | Jun 26 (44 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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