iussp2005 presentation1
TRANSCRIPT
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Statistical Modelling and Causality
Federica Russo*, Michel Mouchart**, Michel Ghins*, Guillaume Wunsch***
* Institut Supérieur de Philosophie, Université Catholique de Louvain** Institut de Statistique, Université Catholique de Louvain*** Institut de Démographie, Université Catholique de Louvain
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Structure of the paper Scientific knowledge Data Causality and statistical modelling
The statistical model Statistical inference and structural models Conditional models and exogeneity Beyond exogeneity Hypothetico-deductive methodology
The population and the individual
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Scientific knowledge We are moderate realists:
Models grant cognitive access to some unobservable parts of reality
Modelling is constructing a simplifiedrepresentation of a complex reality
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Data To acquire causal knowledge
we try to make sense of observations
However, collecting data isproblematic under several respects
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Causality and statistical modelling The statistical model
A stochastic representation of the world Analyze data asas a realization
of a family of distributions
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Causality and statistical modelling Statistical inference and structural models
Structural models make statistical inferenceoperational and meaningfulthrough a learning-by-observinglearning-by-observing process
StructuralStructural models: a representation of theworld that is stable under a large classof interventions
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Causality and statistical modelling Conditional models and exogeneity
p (x | ) = p (z | ) p (y | z , )
The conditional part is structuralstructural and represents the data generating process
Z is an exogenousexogenous variable in a structural model,that is Z is a causalcausal variable
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Causality and statistical modelling
Causality: exogeneity in a structural model
operational concept, internal to the model
Beyond exogeneity
Temporal and atemporal features grant causality
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Hypothetico-deductive methodology Theorizing out-of-sample information
Choice of variablesFormulation of the causal hypothesis
Iteratively: Building the statistical model Testing the adequacy model-data
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The population and the individual Methodological issue:
Detect causal variables, provide a sufficient list Describe the causal mechanism
Practical issue: Take decisions about individualsindividuals
based on knowledge about the populationpopulation
Epistemological issue: Causal knowledge about the population
guides causal attribution about individualsthrough Bayes’ theorem
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To conclude… Causality: exogeneityexogeneity in a structural model
Thus defined, causality is internalinternal to the model
Structural models mediateepistemic accessepistemic access to causal relations