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Learner Reviews & Feedback for Causal Inference by Columbia University

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About the Course

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 - 25 of 27 Reviews for Causal Inference

By Byron S

Oct 30, 2018

By Seo-Woo C

May 15, 2019

By John S

Feb 3, 2020

By Yurong J

Apr 19, 2020

By Max B

Nov 26, 2018

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Aug 4, 2019

By Raghav B

Jan 5, 2021

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Jun 6, 2019

By Guannan Y

Aug 25, 2020

By James M

Jan 24, 2022

By Vladislav K

Dec 12, 2020

By Charles H

Dec 16, 2018

By Yanghao W

Apr 18, 2020

By Fabio M

Mar 29, 2021

By Steve N

May 15, 2020

By Vikram D

Aug 28, 2022

By Germán A

Jan 9, 2021

By Maxim V

Apr 8, 2022

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Jun 12, 2020

By Víthor R F

Jan 16, 2020

By Weijia C

Jul 12, 2020

By Zerui Z

Dec 12, 2021

By Yizhi L

Apr 10, 2021

By Dale S

Apr 26, 2021

By Cecil C L

May 5, 2021