Chevron Left
Back to Causal Inference 2

Learner Reviews & Feedback for Causal Inference 2 by Columbia University

3.4
stars
14 ratings

About the Course

This course offers a rigorous mathematical survey of advanced topics in 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 advanced topics in causal inference, including mediation, principal stratification, longitudinal causal inference, regression discontinuity, interference, and fixed effects models....
Filter by:

1 - 6 of 6 Reviews for Causal Inference 2

By Ang K J

•

Aug 24, 2020

Reading off the slides, no real explanation of concepts or notes provided.

By Samuel D

•

Apr 5, 2020

No engaging. Unable to complete the tasks

By Seethu S

•

Nov 27, 2020

Excellent treatment of mediation, regression discontinuity, longitudinal causal inference, interference and fixed effects. This course has whetted my appetite to dig in to the relevant statistics literature in more detail. The potential outcomes framework is so powerful in terms of delineating causal assumptions and clearly setting up identification conditions for empirical estimation of causal effects.

By Huyen N

•

May 1, 2020

This course is painful. Lots of dry maths with no relatable examples. Difficult to follow.

By Weijia C

•

Aug 16, 2020

Too few and easy assessment questions that does not help understand the course much

By Vikram M

•

Oct 21, 2019

Terrible

Coursera Footer

Learn Something New

Popular Data Science Courses

Popular Computer Science & IT Courses

Popular Business Courses

Coursera

Community

More

Learn Anywhere
Placeholder
Placeholder
Placeholder