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X-WR-CALDESC:Events for HumTech - UCLA
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DTSTART:20160313T100000
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DTSTART;TZID=America/Los_Angeles:20160524T140000
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SUMMARY:Causal Inference in Statistics: Or\, What every student should know about causal inference\, and why it is not taught in Statistics 101
DESCRIPTION:Judea Pearl\nComputer Science and Statistics\nUCLA\nCausal Inference in Statistics: Or\, What every student should know about causal inference\, and why it is not taught in Statistics 101\nRecent 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 disciplines\, especially in the health\, social and behavioral 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 mathematics can tell us about “external validity” or “generalizing from experimental studies” 2. Why missing data is a causal problem\, and why it matters.
URL:https://humtech.ucla.edu/event/causal-inference-in-statistics-or-what-every-student-should-know-about-causal-inference-and-why-it-is-not-taught-in-statistics-101/
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