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X-WR-CALNAME;VALUE=TEXT:StochStatMIT: Edo Airoldi
PRODID:-//Harvard events data//EN
BEGIN:VEVENT
UID:event_1122292_0
SUMMARY:StochStatMIT: Edo Airoldi
DESCRIPTION:Title: Some Fundamental Ideas for Causal Inference on Large NetworksAbstract: Classical approaches to causal inference largely rely on the assumption of "lack of interference", according to which the outcome of each individual does not depend on the treatment assigned to others. In many applications, however, including healthcare interventions in schools, online education, and design of online auctions and political campaigns on social media, assuming lack of interference is untenable. In this talk, Prof. Airoldi will introduce some fundamental ideas to deal with interference in causal analyses, focusing on situations where interference can be attributed to a network among the units of analysis, and offer new results and insights for estimating causal effects in this context.IDSS siteStochStat page
LOCATION:32-141
STATUS:CONFIRMED
DTSTART:20150925T150000Z
DTEND:20150925T160000Z
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