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X-WR-TIMEZONE:America/Vancouver
X-WR-CALNAME:Operationalizing Counterfactual Metrics: Incentives\, Ranking\
 , and Information Asymmetry - Serena Wang\, PhD Student\, UC Berkeley
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TZID:America/Vancouver
TZUNTIL:20251102T090000Z
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DTSTART:20231105T020000
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
RDATE:20241103T020000
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RDATE:20250309T020000
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DTSTAMP:20260607T033208Z
CLASS:PUBLIC
CREATED:20231116T213947Z
DESCRIPTION:Abstract: From the social sciences to machine learning\, it has
  been well documented that metrics to be optimized are not always aligned 
 with social welfare. In healthcare\, Dranove et al. (2003) showed that pub
 lishing surgery mortality metrics actually harmed the welfare of sicker pa
 tients by increasing provider selection behavior. We analyze the incentive
  misalignments that arise from such average treated outcome metrics\, and 
 show that the incentives driving treatment decisions would align with maxi
 mizing total patient welfare if the metrics (i) accounted for counterfactu
 al untreated outcomes…
DTSTART;TZID=America/Vancouver:20231130T103000
DTEND;TZID=America/Vancouver:20231130T113000
LAST-MODIFIED:20231129T220432Z
LOCATION:UBC Vancouver Campus\, ICCS X836
SUMMARY:Operationalizing Counterfactual Metrics: Incentives\, Ranking\, and
  Information Asymmetry - Serena Wang\, PhD Student\, UC Berkeley
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/operationalizing-counterfactual-metrics-ince
 ntives-ranking-and-information-asymmetry-serena
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