學生對 宾夕法尼亚大学 提供的 A Crash Course in Causality: Inferring Causal Effects from Observational Data 的評價和反饋

4.7
177 個評分
61 個審閱

課程概述

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!...

熱門審閱

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.

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.

26 - A Crash Course in Causality: Inferring Causal Effects from Observational Data 的 50 個評論（共 61 個）

Aug 04, 2019

This course is very suitable for beginners, clear and easy to understand.

May 30, 2019

Good introductory course. 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

Aug 23, 2019

The course itremendoulsy straightened my knowledge of causal evaluation

Jul 08, 2019

Awesome!!! Looking forward to the next one!!!

Sep 01, 2019

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. This course provided me with a solid understanding through illustrations and examples. This has changed my perception that experiments are the only answer to tease out a causal effect. Thank you Jason.

Sep 04, 2019

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. The examples in R were reasonably easy to follow and reproduce even for someone who has not used R (me).

Sep 24, 2019

A clear and straight-to-the-point introduction to causality. I'm really enjoying the course!

Nov 27, 2019

I learned so much from Dr. Roy by watching his great lectures. Thank you!

Sep 11, 2017

enjoyed it very much

Mar 14, 2018

Great course for getting your hands dirty with some real causal methods.

Oct 08, 2019

Very practical for beginners in causal inference

Sep 26, 2017

The best lecture series of causality

Dec 01, 2019

Taking this course was a great help for me in my work. 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. Thank you for using examples also from the field of social sciences. All in all, thank you for making this course!

Aug 24, 2019

Well structured to provide solid understanding of fundamentals, good intuition, and a basic view of applying the covered material.

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.

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.

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.

Feb 27, 2019

The course is very useful for beginners. The materials are clear and easy to understand.

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.

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).

Jul 15, 2018

Excellent course. Could use a small restructuring, as I had to go through the material more than once, but otherwise, very good material and presentation.

Aug 29, 2018

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

Oct 21, 2018

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

Thanks!

May 06, 2018

I have an economics background and during my undergraduate studies I took several statistics and econometric courses. The contents delivered in this course complemented my knowledge very well from another point of view. I would definitely enjoy a more advanced course dealing with other methods. The only aspect I would improve is providing the slides for further study. Other courses in Coursera do this and, honestly, I often consult the slides.

Jul 02, 2017

In the beginning the course to me was quite difficult, as it has a different perspective on statistics I was used to. Most people tend to say: "correlation is not causality". When it came to propensity scores, matching and so on the possibilities became more clear to me to apply these methods in practice. The pace of the videos is slow, so I played the videos in 1.5 of the time. What I missed was the ability to download the slides. The instructor would look into this, but we're still waiting several weeks later. Another thing I missed was any sense how many other students were in the course.