Causal Discovery | Inferring causality from observational data

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This is the final video in a three-part series on causality. In it, I sketch some big ideas from causal discovery, which aims to infer causal structure from data. I finish with a concrete example of doing causal discovery in Python.

Resources:

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Introduction - 0:00
Causal Discovery - 0:21
Forward/Inverse Problem - 1:09
3 Tricks of Causal Discovery - 2:28
Trick 1: Conditional Independence Testing - 2:32
Trick 2: Greedy Search of DAG Space - 5:01
Trick 3: Exploiting Asymmetries - 8:23
Trick-based Taxonomy - 10:34
Example: Causal Discovery with Census Data - 11:13
Closing remarks - 14:26

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Thank you very much for this playlist! I'm doing my bachelor thesis on Learning Causal Structures form observational data and your videos are a great introdution.

zahrasaremi

THAT is veryyy helpful. Thanks a lot!!!

Temporary_handle_

Great series. If different algorithms give different causal models then how do we choose between them? Any suggestions?

shaikfiaz

Thank you for the Great video!
I have a question on how to learn DAG when the variables in the data are continuous not just discrete (Boolean or categorical)?

habibollalatifizadeh

What do you think of latent causal variable? Doesn’t it make the problem into a black box?

wryltxw

How about rank deficient causal discovery? Can you consider that for future.

wryltxw

Looks like different algorithm give different graphs. How to validate them? How to decide which is the best?

karannchew

Hello, I am trying to learn more about Causality. What I understood is often to solve causal inference (which is focused on relationship between two variables), we need a causal graph. Especially if I want to use structural causal model approach and not potential outcome framework. And a way to get the causal graph is to have strong domain knowledge. And causal Discovery method explained here seems to be another way. Please let me know if my understanding is correct.

roopalilalwani

Are there metrics to validate causal models built through causal discovery?

rohansinghwilkho

This is really great :-) I wonder though if it works for time-series and one-time events. For example, can you infer the effect of an event (a tweet from Bidon) to an uprise of S&P index?

TheTessatje

In GES algorithm how are the direction of the edges determined?

oiendrilabasak

"If you give someone a graduate degree it's not going to have any affect on their age"
My grey hairs beg to differ!

soupizcool

So based on the example in this video I think I can reasonably conclude that all 3 casual discovery "ticks" are more or less garbage at finding causal DAGs.

ares

You ever get nervous before reading a name?

King_Konglish

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