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UID:20250513T174243EDT-63816iD5TE@132.216.98.100
DTSTAMP:20250513T214243Z
DESCRIPTION:TITLE\n\nCausal inference for the expected number of recurrent 
 events in the presence of a terminal event.\n\nABSTRACT\n\nWhile recurrent
  event analyses have been extensively studied\, limited attention has been
  given to causal inference within the framework of recurrent event analysi
 s. We develop a comprehensive efficiency theory for causal inference in re
 current event data with a terminal failure time. We define our estimand as
  the vector comprising both the expected number of recurrent events and th
 e failure survival function evaluated along a sequence of landmark times. 
 We show that the estimand can be identified under a weaker condition than 
 conditionally independent censoring and derive the nonparametric efficient
  influence function under general censoring and failure distributions with
 out assuming absolute continuity. We will assess the causal effect of air 
 pollution exposure on the recurrence of cardiovascular disease-related hos
 pitalization using Medicare data from Arizona.\n\nPLACE\n	Hybride - CRM\, S
 alle / Room 5340\, Pavillon André Aisenstadt\n\nLien ZOOM Link\n
DTSTART:20250502T193000Z
DTEND:20250502T203000Z
SUMMARY:Ashkan Ertefaie (University of Pennsylvania)
URL:/mathstat/channels/event/ashkan-ertefaie-universit
 y-pennsylvania-365159
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