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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

309 個評分
103 條評論


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!...



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.


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)

創建者 Rudy M P


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

創建者 Alessandro C


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

創建者 keyvan R


great course and practical introduction to causal inference.

創建者 Ziyang H


A good course with detailed explanation and data examples

創建者 Mohammed S U


Excellent course in causal effect estimation. Thanks .

創建者 Aniket G


Superb crash course for quickly getting up to speed!

創建者 Marriane M


Very practical for beginners in causal inference

創建者 Min-hyung K


Thanks so much for providing this great lecture.

創建者 Arka B


gives thorough basic intro to causal inference

創建者 Michael S


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

創建者 Tarashankar B


Detailed and excellent course on causality

創建者 Pichaya T


Excellent courses. I gain my expectations.

創建者 Akin A C


excellent course, very very useful!!

創建者 Takahiro I


The best lecture series of causality

創建者 Clancy B


no nonsense, in depth and practical

創建者 Paulo Y C


intense and well crafted course!

創建者 William L


wonderful course, very helpful

創建者 Bob H


Good intro of the techniques.

創建者 Junho Y


Jason Roy! He is a monster!

創建者 Xisco B


Very interesting studies.

創建者 Andreas N


Very well presented.

創建者 Chang L


enjoyed it very much

創建者 Jose S



創建者 Alfred B


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!

創建者 Marko B


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.