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Avis et commentaires pour d'étudiants pour Inférence causale par Université Columbia

50 évaluations
20 avis

À propos du cours

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 - 20 sur 20 Avis pour Inférence causale

par Byron S

30 oct. 2018

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

par Seo-Woo C

15 mai 2019

It was difficult to follow lectures without any kind of reading

par Max B

26 nov. 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

par Yurong J

19 avr. 2020

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

par John S

3 févr. 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.

par Charles H

16 déc. 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.

par Lucas B

6 juin 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.

par Agnes v B

4 août 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.

par Yanghao W

18 avr. 2020

More exercises would be better!

par Guannan Y

25 août 2020

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

par Vladislav K

12 déc. 2020

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

par Steve N

15 mai 2020

I can't unsubscribe.

par Germán A

9 janv. 2021


par Pablo A G V

12 juin 2020

Great course. Really interesting and condensed content. However, It was difficult to follow lectures without any kind of reading and there wasn't any support on the discussion forums.

par Víthor R F

16 janv. 2020

The teacher is great, but some things could be explained more clearly. Also, there are some errors in the assignments. Other from that, totally worth it!

par Fabio M

29 mars 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).

par Weijia C

12 juil. 2020

Lectures are informative, test questions practical. Whereas more delibration could be used to the writing of assessment questions and answers as there are obvious errors. Also, forum is not well-maintained leaving many questions unanswered for years.

par Yizhi L

10 avr. 2021

the teaching videos are kind of boring

par Harsha G

21 mars 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.

par Raghav B

5 janv. 2021

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