Grant: Discovery Grants Program - Individual
Université du Québec à Montréal
$15,000.00
Model selection in causal inference
FY 2018-19
Summary
Analysts use several statistical techniques, all requiring the specification of a model, in order to determine the true effect of an exposure (e.g., a drug) on a population. In this research program we argue for a change of practice, from the choice of a single model to one where an 'average' model is used. This 'average' model combines information from the most plausible models instead of using only the 'best' model. We will study the efficacy of the model averaging approach combined with various techniques currently used to determine the real effect of an exposure on a variable of interest.*
Program
Discovery Grants Program - Individual
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
356000-2013
Name nom
Lefebvre, Geneviève
Department département
Mathématiques
Organization id
57
Institution établissement
Université du Québec à Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2018
Competition year année de concours
2013
Award amount
15000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
1200
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Advancement of knowledge
Area of application fr
Avancement des connaissances
Research subject code
3000
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Statistics and probability
Research subject fr
Statistique et probabilité
Partie
6
Nb partie
6
Application title
Model selection in causal inference
Keyword
NSERC
Application summary
Analysts use several statistical techniques, all requiring the specification of a model, in order to determine the true effect of an exposure (e.g., a drug) on a population. In this research program we argue for a change of practice, from the choice of a single model to one where an 'average' model is used. This 'average' model combines information from the most plausible models instead of using only the 'best' model. We will study the efficacy of the model averaging approach combined with various techniques currently used to determine the real effect of an exposure on a variable of interest.*
