About this course
Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.
Thinking about causality
1m 43sWhat you should know
1m 34sThe investigator, the jury, and the judge
3m 7sFisher and experiments
4m 59sJohn Snow and natural experiments
7m 42sDouble blind studies
2m 15sControl variables (ANCOVA)
11m 11sJudea Pearl: Problems with control variables
2m 41sModeration, mediation, and lurking variables
6m 45sSimpson's paradox
8m 41sChallenge: Moderation, mediation, or a third variable
2m 15sSolution: Moderation, mediation, or a third variable
3m 27sTuring, Enigma, and CAPTCHA
5m 53sEnigma and uncertainty
4m 7sDeveloping an intuition for Bayes with Wordle
8m 36sWordle and conditional probability
4m 26sWordle, bans, and bits
5m 7sWordle and Bayes' theorem
4m 40sChallenge: Conditional probability and Bayes' theorem
1m 43sSolution: Conditional probability and Bayes' theorem
2m 45sContrasting frequentist statistics and Bayesian statistics
4m 54sBayesian T-Test with JASP
12m 20sGoogle Optimize
3m 42sBayes and rare events
4m 26sChallenge: JASP
2m 1sSolution: JASP
4m 16sSewell Wright
4m 23sIntroducing path analysis and SEM
2m 55sSEM example: Intention
4m 16sMyths about SEM
4m 16sLatent variables in SEM
2m 49sFinding direction of causality with SEM (PSAT)
3m 35sJudea Pearl and the causal revolution
4m 43sDownloading BayesiaLab and resources
2m 37sIntroducing BayesiaLab: Hair and eye color
3m 47sIntroduction to causal modeling with Bayesian networks
6m 27sBayesian Networks: Black Swan case study
3m 33sTaking causality further
2m 37s