Ang Kang Jie
1.0
Reviewed Aug 24, 2020Reading off the slides, no real explanation of concepts or notes provided.
Causal Inference 2
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.
Status: Statistical Analysis
Status: Regression Analysis
AdvancedCourse6 hours
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Samuel Danilola
1.0
Reviewed Apr 5, 2020No engaging. Unable to complete the tasks
Seethu Seetharaman
5.0
Reviewed Nov 27, 2020Excellent 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.
Huyen Nguyen
3.0
Reviewed May 1, 2020This course is painful. Lots of dry maths with no relatable examples. Difficult to follow.
Weijia Chen
3.0
Reviewed Aug 16, 2020Too few and easy assessment questions that does not help understand the course much
Vikram Mullachery
1.0
Reviewed Oct 21, 2019Terrible