BEGIN:VCALENDAR
PRODID: University of Exeter
VERSION:2.0
BEGIN:VTIMEZONE
TZID:Europe/London
X-LIC-LOCATION:Europe/London
BEGIN:DAYLIGHT
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
TZNAME:BST
DTSTART:20221019T140000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
TZNAME:GMT
DTSTART:20221019T140000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
END:STANDARD
END:VTIMEZONE
METHOD:PUBLISH
BEGIN:VEVENT
SUMMARY; CHARSET=UTF-8 :Causal Inference: Introduction to Partial Identification and Recent Advances by Jakob Zeitler, PhD Candidate in Foundational Artificial Intelligence at the Centre for Doctoral Training in Foundational AI at UCL, London
UID:exeter_event_12456
URL:http://www.exeter.ac.uk/events/details/?event=12456
DTSTART;VALUE=DATE:20221019T140000
DTEND;VALUE=DATE:20221019T150000
X-MICROSOFT-CDO-ALLDAYEVENT:FALSE
ORGANIZER: MAILTO:
ATTACH: http://www.exeter.ac.uk/events/details/?event=12456
DTSTAMP:20221013T141112
LOCATION:Streatham Court Old C 
DESCRIPTION; CHARSET=UTF-8 :Abstract:

Causal inference provides the fundamental causal reasoning that machine learning is missing to effectively tackle decision making problems. So far, full identification of causal effects has been the focus of the majority of research: Strong and mostly untestable assumptions, such as no unmeasured confounding, yield point estimates of how a sprint will increase my endurance by 2% or how $10k more in savings will get my loan application accepted. Ideally, we would want to make fewer strong assumptions, but still provide informative suggestions.  http://www.exeter.ac.uk/events/details/?event=12456
SEQUENCE:0
PRIORITY:5
CLASS:
STATUS:CONFIRMED
TRANSP:TRANSPARENT
X-MICROSOFT-CDO-IMPORTANCE:1
X-Microsoft-CDO-BUSYSTATUS:FREE
X-MICROSOFT-CDO-INSTTYPE:0
X-Microsoft-CDO-INTENDEDSTATUS:FREE
END:VEVENT
END:VCALENDAR