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SUMMARY:Samantha Kleinberg (Stevens Institute of Technology)
DTSTART:20201209T180000Z
DTEND:20201209T190000Z
DTSTAMP:20260423T003253Z
UID:MPML/29
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/29/">Da
 ta\, Decisions\, and You: Making Causality Useful and Usable in a Complex 
 World</a>\nby Samantha Kleinberg (Stevens Institute of Technology) as part
  of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstrac
 t\nThe collection of massive observational datasets has led to unprecedent
 ed opportunities for causal inference\, such as using electronic health re
 cords to identify risk factors for disease. However\, our ability to under
 stand these complex data sets has not grown the same pace as our ability t
 o collect them. While causal inference has traditionally focused on pairwi
 se relationships between variables\, biological systems are highly complex
  and knowing when events may happen is often as important as knowing wheth
 er they will. In the first half of this talk I discuss new methods that al
 low causal relationships to be reliably inferred from complex observationa
 l data\, motivated by analysis of intensive care unit and other medical da
 ta. Causes are useful because they allow us to take action\, but how there
  is a gap between the output of machine learning and what helps people mak
 e decisions. In the second part of this talk I discuss our recent findings
  in testing just how people fare when using the output of machine learning
  and how we can go from data to knowledge to decisions.\n
LOCATION:https://researchseminars.org/talk/MPML/29/
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