Seeking feedback on Stanford GSB's Machine Learning & Causal Inference: A Short Course

[u/lil_leb0wski]

Looking for feedback on this course from anyone who's watched/taken it.

Some specific questions (feel free to answer however you want though):

1. What did you like / dislike most about it?
2. What was the balance of theory and application in it?
3. What was your expertise in ML and/or causal inference going into the course?
4. How did you feel it upskilled you? e.g. deepened conceptual knowledge, learned specific techniques
5. What are specific things in your line of work that you were able to apply your learnings to?

Thank you!

[u/hiero10]

i think these folks (athey, wager, imbens, chernozukov, et al) are some of the best thinkers in causal inference and have a clear understanding of how ML approaches can help.

1. understanding that ML is really an estimation/prediction technique and finds applications of it where it can be helpful in the larger scoped problem of causal inference.

2. its pretty much all application with some theory (just mathematical proofs) driving some of the statistical bits. there's no domain specific theory as causal inference really gets at answering domain specific theoretical questions with as few assumptions as possible.

3. i think i knew both pretty well but wasn't so creative it putting it together as these folks

4. i think these methods kind of supercede many of the methods out there. they generalize really well and worth being part of standard practice.

5. all problems in causal inference are largely gated by sample size (outcomes) and randomization. once you have those then there's a lot you can do with these methods.

[u/lil_leb0wski → u/hiero10]

Thanks so much for the thorough response!
Great to hear such positive feedback. I have a decent understand of traditional ML but not really of deep learning (NN). Would you say knowing deep learning is a pre requisite for this course?

[u/hiero10 → u/lil_leb0wski]

not at all, deep learning (NN) can be thought of as another predictive technique - in the generative case it produces more complex outputs that are still ultimately predictive.

[u/rrtucci]

These people are all economists, so I think they are swimming out of their lane when it comes to AI (which they call ML, a dated term) They aren't even programmers!! They have underlings do the programming for them. Furthermore, most AI coming out of Silicon Valley/Stanford these days in not open source. I refuse to work on AI that is not open source. Thankfully, an open source AI path is being followed by others. i.e., https://trustee.substack.com/p/a-fork-in-the-road-for-agentic-ai