10 - Causal Discovery from Observational Data

In the 10th week of the Introduction to Causal Inference online course, we cover causal discovery from observational data. Please post questions in the UA-cam comments section.
Introduction to Causal Inference Course Website: causalcourse.com
0:00 Intro
1:14 Outline
1:48 Assumptions for Independence-Based Causal Discovery
7:15 Markov Equivalence and Main Theorem
18:44 The PC Algorithm
32:43 Can We Do Better?
34:17 Issues with Independence-Based Causal Discovery
35:24 No Identifiability Without Parametric Assumptions
39:40 Linear Non-Gaussian Setting
46:48 Nonlinear Additive Noise Setting

[@96takis]

Hello and thank you for the course. The guest talk from Jonas Peters is not in the channel's uploads. Can we find it somewhere else? thank you!

[@hwiiin]

Hi Brady, thanks for your course! I appreciate it very much. A question for the beginning example, isn't A and D are d-seperated by B and C? Another question is for the skeleton example, the X1-> X2

[@wimcasteels9917]

Thanks for the nice lecture! Where can I find the guest talk of Jonas Peters?

[@josemanuelriveraespejo3259]

I'm looking for the same.

[@xingwenliu6677]

Thank you for your wonderful course! I'm wondering if the nonlinear-gaussian assumption works for causal identification is coming from the ''Conjugation properties'' of Gaussian distribution, as gaussian belongs to exponential family. This is just my naive intuition, I will check the proof and refs later! thanks again!

[@BradyNealCausalInference]

I'm not sure haha

[@florisroos2132]

Nice meme at 6:54 😉

[@mayankagarwal2868]

How to reach to the solutions of the questions you mention in between?

[@programmingwithjackchew903]

Hi sir how to interpret undirected edges in essential graphs after executing PC

[@programmingwithjackchew903]

hi, sir what if after executing the PC it shows the undirected graph, for example, A and B a line with no orientation on either side? How can I interpret this?

[@videohub9521]

Can you explain pcmci algorithm please?

[@woolee8809]

Thank you for your video. btw, where can I find pdf of this chap?

[@BradyNealCausalInference]

There's the course book: www.bradyneal.com/causal-inference-course#course-textbook
This chapter should come out in 1-2 days. I'll probably send an email out once it's ready on the course mailing list here: www.bradyneal.com/causal-inference-course#course-mailing-list
Alternatively, you can watch the course Slack.

[@woolee8809]

@@BradyNealCausalInference Thanks, Brady!

[@videohub9521]

Is this algorithm available in python?

[@MarshmallowIceCream]

can i give u 2 likes?