Retour à Inférence causale

3.6

étoiles

32 évaluations

•

12 avis

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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par Byron S

•Oct 30, 2018

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

par 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

par Yurong J

•Apr 20, 2020

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

par Seo-Woo C

•May 15, 2019

It was difficult to follow lectures without any kind of reading

par 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.

par Lucas B

•Jun 06, 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 Yanghao W

•Apr 18, 2020

More exercises would be better!

par Agnes v B

•Aug 04, 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 John S

•Feb 03, 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 Pablo A G V

•Jun 12, 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

•Jan 16, 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 Stephen N

•May 15, 2020

I can't unsubscribe.