Balaban Causality - UiO
Transcript of Balaban Causality - UiO
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Causality in Machine LearningFor the IFI MLS reseach seminar, May 22, 2020
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Causality is relevant in several areas of machine learning
1. Moving from ML towards AI
2. Making better predictions
3. Learning causal relationships from data
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ML algorithms crunch data based on an objective
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Making algorithms aware of causality could be an important steptowards a true artificial intelligence
Humans reason about causes and effects
f(x) = ….
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Causality has been formalized in terms of counterfactuals and directed acyclic graphs(DAGS)
DAGCounterfactuals
"If patient X had not smoked she would not have gotten lung cancer"
OXS = 1 = 0
"If all Norwegians smoked 10% of them would get cancer within 10 years"
E[ONorway] S = 1 = 0.1
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The ideal dataset for inferring causality is a randomized trial
50/50
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Observational datasets contain causal relationships which can make or break your ML predictions
side effect
confoundercollider
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Reweighing or resampling datasets can help to address confounding and selection issues
men
woman
men woman
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The real world contains many feedback loops, so the timing of measurements is important
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The field of causal discovery deals with inferring causal relationships from data
How to learn the arrows if you are given just data A-I ?
Check out the review article by Bernhard Schölkopfhttps://arxiv.org/pdf/1911.10500.pdf
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Are there any causality issues in the ML problems that you work with?
side effect
confoundercollider