14. Causal Inference, Part 1 

Brilliant! Thank you for the video and I feel very blessed to be borned in this age when such brilliant lectures are available for free for everyone!

This is the most intuitive and comprehensive guide on causal inference. Thank you Prof. Sontag.

Really nice explanations, you kept it simple in the beginning but explained the gist! Thanks for uploading these lectures

What a fantastic teacher and the lecture itself. Thanks for posting this, although I am pretty late to get to it!!

The best lecture on causal inference online

Thank you so much for sharing it with us. It was amazing :)

Simply a great lecture. I just recently started diving into this field, and with this lecture I think I have learned the most so far.

wow, awesome intro to causal inference!

Amazing lecture - Thanks!

Superb introduction for a non-mathematician “domain expert” To understand what the technical expert needs. Unfortunately the underlying quality of the real world data we work with often is insufficiently standardized or machine actionable. This technology is needed for the problems that actually occupy most of a physicians time which is predicting and assessing the effects of treatments particularly once we get off the original guidance from guidelines which might not work in an individual patient.

Fantastic explanation! Imma make a video on this topic too.

Perfect!

Wonderful!!

really a great lecture!

Great explanation. I wish I had teachers like him.

Fantastic!

I have two question and will be grateful for expert and practicioner answers: 1) When calculating CATE you subtract two regressions. This must increase the error considerably. Do we do anything about it? 2) I think in practice, when defining the parameters/independent variables, there's a risk of Simpson paradox. E.g. where's the line between exercising (1) and not exercising (0)? What can one do about it to sleep calmly? Could we do some sort of "hyperparameter tuning" to find the best parameter definitions? It can be tricky...

Nice!

What if a confounder variable only influences the outcome？it's a violation or not

At the 12:30 mark, X₂←X₁→X₃ is described as a v-structure that can be distinguished from a chain structure with data. That's not a v-structure in that sense, you would need X₂→X₁←X₃.

Where does the counterfactual data come from?

I would say that Y1 is the red pill and Y0 blue, not the other way

Question: how do we infer the graphical causal model from data? In the lecture, and the one that follows, we assume a model already exists and use data to answer questions about this model. There are no model selection or model checking involved. Is there a way to infer the causal model from observational data?

Are the problem sets for the course available!? Can't seem to find them

can I translate in arabic (dubbing ) for our students

Less than 10 minds of the video, I feel like there would be overtreatment of the patient given predictive results. Do more is better than do less

This professor is so handsome.

Bad teaching. Non-coherent. Difficult to follow.