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学生对 Columbia University 提供的 Causal Inference 的评价和反馈

3.4
100 个评分

课程概述

This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships. We will study methods for collecting data to estimate causal relationships. Students will learn how to distinguish between relationships that are causal and non-causal; this is not always obvious. We shall then study and evaluate the various methods students can use — such as matching, sub-classification on the propensity score, inverse probability of treatment weighting, and machine learning — to estimate a variety of effects — such as the average treatment effect and the effect of treatment on the treated. At the end, we discuss methods for evaluating some of the assumptions we have made, and we offer a look forward to the extensions we take up in the sequel to this course....

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1 - Causal Inference 的 25 个评论(共 38 个)

创建者 Byron S

Oct 29, 2018

Not having access to slides and materials negates any interest in proceeding with this course.

创建者 Seo-Woo C

May 14, 2019

It was difficult to follow lectures without any kind of reading

创建者 John S

Feb 3, 2020

The first week is a throw-away, as there are no slides, just a talking head throwing notation at you. The second week at least has a blackboard, but then the assessment is broken.

创建者 Yurong J

Apr 19, 2020

It is impossible to learn statistics without slides in the first week.

创建者 Max B

Nov 26, 2018

Great course. Really interesting and condensed content. A perfect course for analysts and data scientists. I will be recommending this to a few of my colleagues.

For some reason there are no slides in week 1 but don't worry there are slides from week 2 onwards

创建者 James M

Jan 24, 2022

I find it incredible that a course discussing a topic using complicated subscripted variables such as Y sub i sub Z would not use the equivalent of a whiteboard, but would instead try to communicate these concepts vocally. Using a professor who speaks in a monotone.

I am also surprised that apparently no attempt was made to make the suggested readings from the literature available on the web.

创建者 Raghav B

Jan 5, 2021

Please add slides or some teaching aids. This course is otherwise not usable

创建者 Vladislav K

Dec 12, 2020

Talking head is not the best way to present for presenting such subjects.

创建者 Agnes v B

Aug 4, 2019

It is a very good intro to CI with proofs and references to recent developments.

However, I have to subtract some stars because the quality in material preparation of this course is not up to usual Coursera standards: for the first week there are no slides (so it's hard to follow), and some answers in the exams are not correct. This has been pointed out on this course's discussion forums, but nobody involved in the preparation of this course replies on its discussion forums.

创建者 Inspector T

May 6, 2022

Guy reading lectures? Give me a break. This has to be the worst course ever.

创建者 Lucas B

Jun 6, 2019

A good course. Lot's of insights on Propensity Score Matching. They show good references to those willing to read some articles. Although quick classes, exercises are easy and very practical.

创建者 Guannan Y

Aug 25, 2020

I can't feel any efforts the lecturer had made to help us understand the topic.

创建者 Charles H

Dec 16, 2018

The selection of material is excellent and the professor covers an amazing amount of ground in a handful of lectures. Currently, however, there are many superficial problems with the course, including repeated errors in the quizzes and lectures that are confusing because the slides are missing.

创建者 Fabio M

Mar 29, 2021

Topic/syllabus/reference material: 5 stars - a great intro to CI (Rubin's approach)!!

Learning material: 2 stars (talking head, slides not provided, typos).

Assessment: 1 star (not particularly engaging and full of mistakes like correct answers scored as incorrect or calculations expected to be done with data different from that provided).

创建者 Info D

May 5, 2021

In my experience, this is a course where knowledge is obtained in another way and from outside the course. Confusing and there is no proper, ethereal exposure. This is my exclusive opinion. And for me, it is very sad to take an absolutely useless course, which is why I decide to drop out so as not to waste time.

创建者 Yanghao W

Apr 18, 2020

More exercises would be better!

创建者 Rebecca M

May 6, 2024

Nice overview of topic but many quiz answers were wrong and haven't been corrected in over 5 years.

创建者 Zerui Z

Dec 12, 2021

The layout of the slides is easy to lose people. There are too many errors in the quiz and no one has ever tried to correct them even though some students have been pointing them out for years.

创建者 Yizhi L

Apr 10, 2021

the teaching videos are kind of boring

创建者 Dale S

Apr 26, 2021

I

创建者 Matt T

Mar 17, 2023

This course needs updating/a rework. The topics covered are great, but the assessment problems are out of date, sometimes wrong, or do not accept the correct answer. The ideas behind the syllabus here are exactly what I want from a mid-level/graduate intro to causal inference, but this is one of the worst courses I've ever taken on Coursera.

创建者 Harsha G H

Mar 21, 2021

The course is worse than going through a textbook, the professor's explanation on most of the proofs and statements is "obviously you know this and that". Additionally, the assessment had multiple errors and vague instructions.

创建者 Alfred E

Feb 26, 2024

A talking lecturing on mathematics and statistics without any equations or slides. It's impossible to follow and the worst form of pedagogy. Coursera and Columbia should remove this course. It's embarassing.

创建者 AmiReza M

Dec 25, 2022

Instructor does not use Notes, Whiteboard, etc. to demonstrate points. Written elaboration precedes verbal explanation in a statistics course!

创建者 Steve N

May 15, 2020

I can't unsubscribe.