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LinkedIn Learning

Machine Learning and AI Foundations: Causal Inference and Modeling

via LinkedIn Learning

Overview

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

Syllabus

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

Taught by

Keith McCormick

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4.6 rating at LinkedIn Learning based on 136 ratings

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