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TZID:America/New_York
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DTSTART:20241103T020000
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DESCRIPTION:Donna Spiegelman\, ScD  Yale School of Public Health When do s
 pecialized causal inference methods add value beyond standard approaches?
  Drawing from her unique perspective as both an epidemiologist and a bios
 tatistician\, Donna Spiegelman will consider the circumstances under whic
 h commonly invoked causal assumptions are necessary for causal inferences
  to be validly made from data\, concluding that often not. She will show 
 that valid learning can occur 1) under conditions much less restrictive t
 han required by current widely used methods\, 2) when real world implemen
 tation of interventions vary\, and 3) when interventions spill over to ot
 hers not directly exposed\, thereby obviating components of the SUTVA ass
 umption. She will provide evidence that measurement error is the major so
 urce of bias in observational research\, not confounding\, whose bias is 
 rather tightly bounded. Finally\, she will discuss the eternal challenge 
 in science: after exhaustive efforts to collect data to predict important
  outcomes\, a substantial proportion of the variation in occurrences of t
 hese outcomes appear to be entirely random. A discussion will follow\, fe
 aturing formal remarks by Lee Kennedy-Shaffer and contributions from Bhra
 mar Mukherjee and others to examine and debate the ideas raised in the le
 cture.\n\nSpeaker:\nDonna Spiegelman\n\nAdmission:\nFree\n\nDetails URL:\
 nhttps://medicine.yale.edu/event/when-are-causal-inference-methods-needed
 -to-answer-causal-questions-panel-discussion-to-follow/\n
DTEND;TZID=America/New_York:20261012T173000
DTSTAMP:20260924T184027Z
DTSTART;TZID=America/New_York:20261012T153000
GEO:41.303509;-72.931937
LOCATION:Winslow Auditorium\, 60 College Street\, New Haven\, CT\, United 
 States
SEQUENCE:0
STATUS:Confirmed
SUMMARY:When are Causal Inference Methods Needed to Answer Causal Question
 s? Panel Discussion to Follow.
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