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Causal Inference

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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Columbia University

Rating 3.0 based on 5 ratings
Length 7 weeks
Effort 6 weeks of study, 3-5 hours per week
Starts Jul 6 (last week)
Cost $49
From Columbia University via Coursera
Instructor Michael E. Sobel
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Math And Logic Probability And Statistics

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although quick classes in one review

Although quick classes, exercises are easy and very practical.

it was difficult in one review

It was difficult to follow lectures without any kind of reading

not having access in one review

For some reason there are no slides in week 1 but don't worry there are slides from week 2 onwards Not having access to slides and materials negates any interest in proceeding with this course.

propensity score matching in one review

Lot's of insights on Propensity Score Matching.

covers an amazing in one review

The selection of material is excellent and the professor covers an amazing amount of ground in a handful of lectures.

including repeated errors in one review

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.

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Coursera

&

Columbia University

Rating 3.0 based on 5 ratings
Length 7 weeks
Effort 6 weeks of study, 3-5 hours per week
Starts Jul 6 (last week)
Cost $49
From Columbia University via Coursera
Instructor Michael E. Sobel
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Math And Logic Probability And Statistics

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