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Recent developments in graphical and counterfactual models have revolutionized the way scientists treat problems involving cause-effect relationships.
I will review concepts, principles, and mathematical tools that were found useful in this revolution and will demonstrate their applications in several data-intensive sciences.
These include questions of confounding control, policy evaluation, causes of effects, misspecification tests, mediation analysis, missing data, external validity and the integration of data 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. What mathematics can tell us about "external validity" or "generalizing across populations"
3. Why missing data is a causal problem, and why it matters.
Reference: http://bayes.cs.ucla.edu/PRIMER/
Judea Pearl is the author of Causality (Cambridge, 2000, 2009) who currently leads efforts to introduce causal inference to statistical education. http://magazine.amstat.org/blog/2012/11/01/pearl/
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