Introduction
Thinking about causality
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What you should know
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1. Experimental Design and Statistical Controls
The investigator, the jury, and the judge
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Fisher and experiments
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John Snow and natural experiments
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Double blind studies
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Control variables (ANCOVA)
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Judea Pearl: Problems with control variables
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Moderation, mediation, and lurking variables
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Simpson's paradox
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Challenge: Moderation, mediation, or a third variable
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Solution: Moderation, mediation, or a third variable
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2. Conditional Probability and Bayes' Theorem
Turing, Enigma, and CAPTCHA
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Enigma and uncertainty
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Developing an intuition for Bayes with Wordle
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Wordle and conditional probability
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Wordle, bans, and bits
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Wordle and Bayes' theorem
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Challenge: Conditional probability and Bayes' theorem
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Solution: Conditional probability and Bayes' theorem
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3. Prediction and Proof with Bayesian statistics
Contrasting frequentist statistics and Bayesian statistics
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Bayesian T-Test with JASP
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Google Optimize
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Bayes and rare events
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Challenge: JASP
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Solution: JASP
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4. Causal Modeling with Structural Equation Modeling (SEM)
Sewell Wright
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Introducing path analysis and SEM
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SEM example: Intention
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Myths about SEM
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Latent variables in SEM
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Finding direction of causality with SEM (PSAT)
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5. Causal Modeling with Bayesian Networks
Judea Pearl and the causal revolution
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Downloading BayesiaLab and resources
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Introducing BayesiaLab: Hair and eye color
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Introduction to causal modeling with Bayesian networks
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Bayesian Networks: Black Swan case study
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Conclusion
Taking causality further
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Ex_Files_ML_and_AI_Foundations_Causal_Inf_Modeling.zip
(179 KB)