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BEGIN:VEVENT
DTSTAMP:20261027T000601Z
DTSTART;VALUE=DATE-TIME:20261015T110000
DTEND;VALUE=DATE-TIME:20261015T120000
SUMMARY:FAIS Seminar: Prof. Jin Tian (MBZUAI): Causal Effect Measures for
  Causal Explanation and Decision-Making
TZID:Europe/London
UID:20261015-8ac672c4a13e9a1601a13efb29ec0978@warwick.ac.uk
CREATED:20261015T095330Z
DESCRIPTION:Abstract: Understanding cause-and-effect relationships is a f
 undamental challenge across the data sciences. Traditional measures\, su
 ch as the Average Causal Effect (ACE)\, summarise causal relationships t
 hrough averages but often obscure important heterogeneity across individ
 uals or subpopulations. In this talk\, we introduce new causal effect me
 asures that capture and interpret this heterogeneity to support causal e
 xplanation and decision-making. We characterise causal heterogeneity thr
 ough statistical moments—variance\, skewness\, and kurtosis—present new 
 formulations for the probabilities of causation\, and introduce new metr
 ics for causal decision-making under multiple actions. We outline identi
 fication and bounding results under common causal assumptions and illust
 rate the application of these measures in real-world settings. Bio: Jin 
 Tian is a Professor of Machine Learning at MBZUAI and Editor-in-Chief of
  the Journal of Causal Inference. His research focuses on causal modelli
 ng\, probabilistic graphical models\, and adversarial robustness\, inclu
 ding work on probabilities of causation and causal effect identification
  in cluster DAGs. He is also an Action Editor for the Journal of Machine
  Learning Research and previously spent nearly two decades on the Comput
 er Science faculty at Iowa State University. He earned his PhD in Comput
 er Science from UCLA after completing a bachelor’s degree in Physics at 
 Tsinghua University.
LOCATION:CS1.04
URL:https://faiseminarswarwick.github.io
ATTACH:https://faiseminarswarwick.github.io
CATEGORIES:Foundations of AI & ML
LAST-MODIFIED:20261015T095330Z
ORGANIZER;CN=Paris Giampouras:
END:VEVENT
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