Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.
Overview
Syllabus
Introduction
- Thinking about causality
- What you should know
- 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
- 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
- Contrasting frequentist statistics and Bayesian statistics
- Bayesian T-Test with JASP
- Google Optimize
- Bayes and rare events
- Challenge: JASP
- Solution: JASP
- Sewell Wright
- Introducing path analysis and SEM
- SEM example: Intention
- Myths about SEM
- Latent variables in SEM
- Finding direction of causality with SEM (PSAT)
- 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
- Taking causality further
Taught by
Keith McCormick