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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

325 個評分
111 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.


26 - A Crash Course in Causality: Inferring Causal Effects from Observational Data 的 50 個評論(共 111 個)

創建者 Vikram M


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

創建者 Vlad V


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. This is a topic every data analyst should know doesn't matter which industry you work or learn.

創建者 Hugo E R R


It is a very useful course that combines conceptual and technical aspects of Applied Causal Inference.

The presentations are very clear, the Examples and Exercises (R-coded) have been very useful for me to practice specific R-packages.

創建者 Pritish K


Great course, especially if you are reasonably familiar with R and basic stats and interested in approaching causal analysis. Word of caution: If you have never used R, you will have trouble getting through some of the assignment.s

創建者 Arnab S


I was a novice in causal analysis. But I needed some education in counterfactual estimation. This course provided me with the necessary knowledge and tools. I especially enjoyed the matching, IPTW and IV chapters. Thank you!

創建者 Alice G


Really wonderful course--I learned so much in the way of theory and practical application in R. Some links need to be updated and it would be best to provide students with answers to worked examples for the quiz questions.

創建者 Anastasia G


A great start for those starting to explore causal inference. The somewhat dry delivery of the lectures is fully compensated by how clear and informative they are.

創建者 Andrew


This course is really fantastic for all levels. Very thorough explanations and helpful illustrations. Many thanks for putting this together!

創建者 Ted L


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

創建者 Mario M


Great introduction. Immediately used new knowledge in current job (marketing data scientist). Recommended course to co-workers.

創建者 JK


To those with some advanced statistics background, this would truly be helpful to catch up econometric thought processes.

創建者 Akorlie A N


Excellent course. This course helped me to develop my intuition on some of the more abstract concepts in causality.

創建者 Hao L


Not only good for bio stats, it has also profound impact to my understanding of a/b testing in the internet world.

創建者 Abdulaziz T B


This is an excellent course taught by a very competent professor in a very simple to understand and intuitive way.

創建者 Georges A


Excellent course, extremely well presented that helps clarify a lot of statistical concepts in an intuitive way.

創建者 Michael L


Excellent overview on causality inference and handling confounders combined with practical examples and R code.

創建者 DR A N


Excellent course! Can make it longer though and cover more details and latest advances and issues :-)

創建者 Dror G


Very enlightening. Well explained, and strikes a great balance between theory & practical aspects.

創建者 Hidemasa O


This course is actually great. It is a basic course but it does not mean it is for an amateur.

創建者 Huyen


The best course on causal inference on Coursera. Lots of examples, easy to follow materials.

創建者 Luca A


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

創建者 Cameron F


Good course on the over view of Causality. Not too technical, but not too light and fluffy.

創建者 Zhixin L


Extremely helpful for people who just started to do research on observational studies!

創建者 Akash G


Amazing Course! Really Helpful. I would love to have a similar full-duration course :D

創建者 Oleksandr P


Great course. It is good for broad set of people with different level of math skill.