# 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
312 個評分
105 條評論

## 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
2017年12月27日

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
2017年11月29日

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.

## 76 - A Crash Course in Causality: Inferring Causal Effects from Observational Data 的 100 個評論（共 105 個）

2019年11月22日

Overall a great course. Better than other courses on causal inference on coursera. However, some of the topics (e.g. within the IPTW and IV methodologies ) were presented in a sort of general manner (intuitive). Which is obviously not a fault of the instructor and is due to the strong research nature of these topics. Personally, I can't think of presenting, for instance, 2SLS or insights on IPTW in more detail within a crash course. Perhaps, increasing the number of weeks to 6 or 7 in order to include more detail on, e.g. 2SLS would be a good idea. What definitely helped to make up for those missed details is the practical examples parts with R. Keep up the good job!

2019年10月12日

Clear course most of the time and a very interesting subject. The teacher covers the concepts from many angles: conceptual understanding, math, examples and R code. I like how there is little "fluff", you learn a lot for the time given and I don't feel any of the concepts covered are unnecessary or esoteric. The only negative is that the course could've benefited from more practical assignments. There are 2 R code assignments: could've been more. I was thinking about giving it a 5 or 4 stars and decided on 4 in case a non-perfect score actually makes the instructor improve the course.

2017年8月24日

Very approachable as someone with a Masters in Statistics, probably tough if you are not comfortable with notation and concepts of intermediate prob/stats. Extremely clear and concise presentation. Coverage of methodology is a little weak, there is not enough discussion of the dangers of doing causal inference on observational data, nor of the dangers of the proposed methods. For instance, propensity score matching is ineffective or even harmful in the face of hidden confounders, which in the real world you almost always have.

2020年9月23日

It is a great course for those who want to better understand how causality works, statistically speaking.

Until the 3rd week the classes are very well exemplified and detailed, great to follow.

Then, it is difficult to follow the explanations, impacts of the models, etc. - a pity.

The interpretation of analysis results, variations and other subtleties is not the focus of the course. If you expect to see analysis and interpretation of results right away, this course is not for you.

2018年5月6日

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.

2020年5月2日

The contents of this course are extremely concise and useful. The course prioritizes some of the important techniques used for causal inference. The practice tests , quizzes and data analysis tests were helpful to learn better. The lectures weren't inspiring or exciting and self-motivation is necessary to be able to stick with it. However, I would recommend this course to anyone interested.

2018年12月9日

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).

2020年9月28日

The material is useful and well-presented by Prof. Roy. Although recipes are provided for solving relevant problems in R, more familiarity with R will be required for applying them. Students should be prepared to develop that familiarity on their own.

2020年6月11日

The course is well structured and the slides are well prepared. Professor clearly explains the formulas and makes you easily understand everything that is written on the slides. However, I would love to see some more examples from the social sciences.

2020年8月31日

Course is great for a general overview! That said, the discussion forums are poorly monitored and one of the exercise datasets needs to be updated. In any case, don't expect more from a Coursera course!

2019年3月16日

Very easy to follow examples and great coverage for such an important topic! The delivery sometimes get repetitive and I wish we talked more about how the uncertainties are derived.

2020年11月21日

A high quality course that delivers what it says in the title. Well-paced introduction to the potential outcomes framework, with a nice balance of theoretical and practical aspects.

2018年12月15日

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.

2018年7月15日

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.

2019年2月10日

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.

2019年6月22日

Some of the materials are bit academical and away from industry, however, I found most of the materials relevant and practical.

2020年8月25日

Great course, the title is exactly what you will get: the basics on inferring causal effects from observational data

2020年1月9日

Great course. I have learned a lot. I just wish to have more programming exercises to cement our knowledge.

2018年8月28日

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

2018年10月21日

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

Thanks!

2019年2月27日

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

2017年11月24日

They could offer more applied exercises in R. But, it was also great.

2020年9月20日

The lectures are good, and they might consider covering more topics.

2020年12月4日

It was well laid out, and overall helpful.

2020年11月27日

A good course. Bit difficult for novices.