# 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
309 個評分
103 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.

## 51 - A Crash Course in Causality: Inferring Causal Effects from Observational Data 的 75 個評論（共 103 個）

2018年4月17日

I learned the basics of causality inference and want even more now!

2020年3月31日

Very clear, it give good intuition also for technical points.

2020年9月1日

great course and practical introduction to causal inference.

2020年7月27日

A good course with detailed explanation and data examples

2020年9月4日

Excellent course in causal effect estimation. Thanks .

2019年12月15日

Superb crash course for quickly getting up to speed!

2019年10月8日

Very practical for beginners in causal inference

2017年6月30日

Thanks so much for providing this great lecture.

2018年5月31日

gives thorough basic intro to causal inference

2019年7月7日

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

2020年9月8日

Detailed and excellent course on causality

2018年2月26日

Excellent courses. I gain my expectations.

2021年1月3日

excellent course, very very useful!!

2017年9月26日

The best lecture series of causality

2018年8月28日

no nonsense, in depth and practical

2020年8月2日

intense and well crafted course!

2020年4月3日

2017年10月19日

Good intro of the techniques.

2020年12月21日

Jason Roy! He is a monster!

2019年5月5日

Very interesting studies.

2020年8月29日

Very well presented.

2017年9月11日

enjoyed it very much

2020年2月22日

Enlightening.

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.