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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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TZID:Europe/Paris
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DTSTART:20201025T030000
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RDATE:20211031T030000
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UID:calendar.23329.field_data.0@www.glad.uniroma1.it
DTSTAMP:20260411T053244Z
CREATED:20210419T151251Z
DESCRIPTION:AbstractA fundamental task of many disciplines\, including econ
 omics\, is to identify causal relationships and use them for explanation o
 r for predicting the effects of policy interventions. A traditional way to
  discover causal relationships is to use randomized experiments. But in ma
 ny cases they are too expensive\, impractical or impossible to conduct.  I
 t is then necessary to infer causal relations from statistical properties 
 of purely observational data. This seminar aims to give an introduction an
 d a brief review of the algorithms for causal discovery that were develope
 d in the past three decades\, with a focus on methods based on graphical m
 odels and on independent component analysis. The presentation will be supp
 lemented by some illustrations and applications to time series economic da
 ta.Short bioAlessio Moneta is associate professor of Economics at the Inst
 itute of Economics\, Scuola Superiore Sant'Anna\, Pisa. His research inter
 ests lie on causal inference in econometrics\, model validation\, applied 
 macroeconomics\, and methodology of economics. His updated cv can be found
  at: https://mail.sssup.it/~amoneta/cv_am_sssup.pdfThe seminar will be onl
 ine on Zoom: https://uniroma1.zoom.us/j/84687230068ID riunione: 846 8723 0
 068 Passcode: 216061
DTSTART;TZID=Europe/Paris:20210426T090000
DTEND;TZID=Europe/Paris:20210426T090000
LAST-MODIFIED:20210419T155157Z
LOCATION:Online
SUMMARY:MORE@DIAG Seminar: Causal inference from observational data: concep
 ts and recent methodological advances - Alessio Moneta
URL;TYPE=URI:http://www.glad.uniroma1.it/node/23329
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