The second video in a 3-part series on causality. In this video I discuss key ideas from causal inference, which aims at answering question about cause-and-effect. I finish with a concrete example with code of doing causal inference in Python.
Series Playlist: [ Ссылка ]
📰 Read more: [ Ссылка ]
💻 Example code: [ Ссылка ]
Resources:
- The Book of Why by Judea Pearl: [ Ссылка ]
- Do-calculus: [ Ссылка ]
- Metalearner paper: [ Ссылка ]
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Homepage: [ Ссылка ]
Introduction - 0:00
Causal Inference - 0:28
3 Gifts of Causal Inference - 1:13
Gift 1: Do-operator - 1:20
Gift 2: Confounding (deconfounded) - 3:22
Gift 3: Causal Effects - 5:51
Example: Treatment Effect of Grad School on Income - 8:05
Closing remarks - 11:12
Causal Inference | Answering causal questions
Теги
causalitycausal inferencecausal discoverymachine learningdata sciencecause and effectcausal effectjudea pearlbook of whynew science of cause and effecteconometricscorrelationcausationPythonDoWhyDo WhyMicrosoftMeta-learnermetalearnerT learnerEconMLdo-operatorconfoundingconfoundertreatment effectexample codeUCIcensus datagrad schoolgraduate schoolRandom Forestinterventionfoundations of data sciencetutorialtutor
