Learner Reviews & Feedback for Essential Causal Inference Techniques for Data Science by Coursera Project Network
About the Course
sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn
techniques that can answer questions like these, which are centered around issues of causality and are solved with causal inference. In this
project, you will learn the high level theory and intuition behind the four main causal inference techniques of controlled regression, regression
discontinuity, difference in difference, and instrumental variables as well as some techniques at the intersection of machine l...
Top reviews
CK
Apr 3, 2025
Instructor is very knowledgeable. Best explanations I've come across for causal inference principles. The labs in R are great and have a "real world" feel to them.
KG
Jan 31, 2021
Decent start to Causal Inference Techniques with sufficient theory for a project.
1 - 9 of 9 Reviews for Essential Causal Inference Techniques for Data Science
By Tom B
•Apr 16, 2021
it's a neat format, but there's not a huge amount of material in the course, unless you can keep the code. A lot of these models would be better as glms not linear models, but that isn't really discussed. it would also be useful to see more on the causal forest, which is the area which interested me in particular
By Keerat K G
•Jan 31, 2021
Decent start to Causal Inference Techniques with sufficient theory for a project.
By Cameron D K
•Apr 3, 2025
Instructor is very knowledgeable. Best explanations I've come across for causal inference principles. The labs in R are great and have a "real world" feel to them.
By Chiara L
•Mar 10, 2022
For someone who's unfamiliar with R and causal inference, this helped a lot with familiarizing but it's too short to go fully in-depth. Would like to have discussed more practical ways to apply these methods to machine learning and when-to-use-which technique
By Jonas R M
•Mar 17, 2025
Great course and hands-on. A bit too fast with the ML part, should've taken more time to explain. Other than that, fun!
By Jiaxing S
•Apr 18, 2025
I wish it was on Python
By Sasmito Y H
•Sep 19, 2022
Delivering the promised essential with adequate value.
By Nersu A
•Aug 19, 2022
no reading material and can't revise the concepts
By seyed r m
•Feb 3, 2022
Good match between lecture/example and tests. It would be better if there were more real world examples and the course included use of applying Causal Inference to time-series data.