Causal Discovery | Inferring causality from observational data

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.

Series Playlist: https://www.youtube.com/playlist?list=PLz-ep5RbHosVVTz9HEzpI4d6xpWsc8rOa

📰 Read more: https://towardsdatascience.com/causal-discovery-6858f9af6dcb?sk=2134f5b56c1ce943afdfebbf9e1dcb45
💻 Example code: https://github.com/ShawhinT/YouTube-Blog/tree/main/causal_discovery

Resources:
- The Book of Why by Judea Pearl: https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X
- Causal Discovery Review: https://www.frontiersin.org/articles/10.3389/fgene.2019.00524/full
- Causal Discovery Toolbox: https://fentechsolutions.github.io/CausalDiscoveryToolbox/html/index.html

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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

Видео Causal Discovery | Inferring causality from observational data канала Shaw Talebi