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Machine Learning and AI Foundations: Causal Inference and Modeling

Jul 22, 2022 • Keith McCormick

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About this course

Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.



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Thinking about causality

1m 43s
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What you should know

1m 34s
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The investigator, the jury, and the judge

3m 7s
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Fisher and experiments

4m 59s
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John Snow and natural experiments

7m 42s
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Double blind studies

2m 15s
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Control variables (ANCOVA)

11m 11s
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Judea Pearl: Problems with control variables

2m 41s
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Moderation, mediation, and lurking variables

6m 45s
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Simpson's paradox

8m 41s
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Challenge: Moderation, mediation, or a third variable

2m 15s
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Solution: Moderation, mediation, or a third variable

3m 27s
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Turing, Enigma, and CAPTCHA

5m 53s
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Enigma and uncertainty

4m 7s
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Developing an intuition for Bayes with Wordle

8m 36s
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Wordle and conditional probability

4m 26s
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Wordle, bans, and bits

5m 7s
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Wordle and Bayes' theorem

4m 40s
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Challenge: Conditional probability and Bayes' theorem

1m 43s
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Solution: Conditional probability and Bayes' theorem

2m 45s
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Contrasting frequentist statistics and Bayesian statistics

4m 54s
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Bayesian T-Test with JASP

12m 20s
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Google Optimize

3m 42s
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Bayes and rare events

4m 26s
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Challenge: JASP

2m 1s
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Solution: JASP

4m 16s
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Sewell Wright

4m 23s
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Introducing path analysis and SEM

2m 55s
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SEM example: Intention

4m 16s
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Myths about SEM

4m 16s
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Latent variables in SEM

2m 49s
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Finding direction of causality with SEM (PSAT)

3m 35s
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Judea Pearl and the causal revolution

4m 43s
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Downloading BayesiaLab and resources

2m 37s
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Introducing BayesiaLab: Hair and eye color

3m 47s
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Introduction to causal modeling with Bayesian networks

6m 27s
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Bayesian Networks: Black Swan case study

3m 33s
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Taking causality further

2m 37s