Judea Pearl, Professor Emeritus, and Computer Scientist in Cognitive Systems Lab at UCLA
Recent developments in graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Paradoxes and controversies have been resolved, slippery concepts have been demystified, and practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics.
The concepts, principles, and mathematical tools that were found useful in this transformation will be reviewed, and their applications in several data-intensive sciences will be demonstrated. These include questions of confounding control, policy analysis, misspecification tests, mediation, heterogeneity, missing data, and the integration of findings from diverse studies.
The following topics will be emphasized:
1. What every student should know about causal inference, and why it is not taught in Statistics 101
2. The Mediation Formula, and what it tells us about "How nature works"
3. What mathematics can tell us about "external validity" or "generalizing across populations"
4. What population data can tell us about unsuspected heterogeneity
5. What causal analysis tells us about recovery from missing data
Reference: J. Pearl, Causality (Cambridge University Press, 2000)