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DTSTART:20221106T020000
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UID:calendar.2404.events_uoft_date.0@www.statistics.utoronto.ca
CREATED:20230331T140847Z
DESCRIPTION:\nWhen and Where: \nMonday, April 03, 2023 10:30 am to 11:30 
 am \n Online \n\nSpeakers \nKate Tilling \n\nDescription: \nCausal inferen
 ce can be attempted using different statistical methods, each of which re
 quire some (untestable) assumptions. Common methods include multivariable 
 regression, propensity scores, g-methods (no unmeasured confounding) and
  instrumental variables (no association between instrument and outcome, o
 ther than via the exposure). Less attention has been given to the impact o
 f selection (e.g. selection into a study, analysis of cases only) or miss
 ing data (e.g. dropout from a study, death due to other causes) on differ
 ent methods for causal inference. Using directed acyclic graphs (DAGs) I w
 ill discuss some of the ways in which bias can occur due to selection or m
 issing data, and methods that might be used to detect or mitigate against
  this bias. Applied work shows evidence of non-random selection into and d
 ropout from studies including ALSPAC and UK Biobank, and I will discuss h
 ow this might impact causal analyses using these datasets.Please join the 
 event.About Kate TillingKate Tilling is Professor of Medical Statistics at
  the University of Bristol and an MRC Investigator. Following a degree in 
 Maths, MSc in Applied Statistics and PhD in Epidemiology she took up a po
 st as lecturer in Medical Statistics at King’s College London, moving to 
 the University of Bristol in 2002. She has subsequently built an interdisc
 iplinary research team in the MRC Integrative Epidemiology Unit and leads 
 a MRC-funded research programme on the development and application of stat
 istical methods for causal inference. \n\nContact Information: \n CANSSI O
 ntario \n\nCategories \n Data Science ARES \n\nAudiences \n FacultyGraduat
 e Students
DTSTART;TZID=America/New_York:20230403T103000
DTEND;TZID=America/New_York:20230403T113000
LAST-MODIFIED:20230331T141437Z
SUMMARY:Selection Bias, Missing Data and Causal Inference
URL;TYPE=URI:https://www.statistics.utoronto.ca/events/selection-bias-missi
 ng-data-and-causal-inference
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