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

Decent start to Causal Inference Techniques with sufficient theory for a project.

I wish it was on Python

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.

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

Great course and hands-on. A bit too fast with the ML part, should've taken more time to explain. Other than that, fun!

Delivering the promised essential with adequate value.

no reading material and can't revise the concepts

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.

The R lab is a bit useful, but the intuition is bad and some are even wrong.