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Russo ined june12

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Russo ined june12

  1. 1. Quest-ce quun modèle?Réflexions philosophiques à partir delanalyse causale en sciences sociales Federica Russo https://blogs.kent.ac.uk/federica Center Leo Apostel, VUB Centre for Reasoning, Kent
  2. 2. AperçuPluralisme méthodologique Quelques exemples de modèle en sciences socialesModèles et causalité Modèles d’association vs modèles causaux Segmentation de l’inférence causaleModèles et variation La logique de construction et test de modèlesModèles et modélisation Une stratégie générale pour la construction des modèles 2
  3. 3. PLURALISME METHODOLOGIQUE 3
  4. 4. Une gamme de modèlesDifférents modèles statistiques Il y en a pour tous les besoinsModèles statistiques en sciences sociales Analyse quantitative de données Recherche de généralités dans ces donnéesComment, nous allons le voir 4
  5. 5. Modèles structurelsModèles à équationsstructurellesDécomposition marginal-conditionnelleInterprétation de ladécomposition etdu graphe associéen termes de mécanisme 5
  6. 6. Modèles multiniveauxModéliser les hiérarchiessocialesUn modèle pour étudier l’effetdes variables d’agrégation surles variables individuelles 6
  7. 7. DAG – graphes acycliques dirigésL’intelligence artificielle revitalisele débat sur la causalitéUne approche algorithmiquebasée sur: Causalité probabiliste Condition Causale de MarkovDe l’IA aux sciences sociales? 7
  8. 8. … et d’autres encore …e.g.: modèles à structure de covariance,tables de contingence, potential outcomemodels, Granger models, …
  9. 9. MODELES ET CAUSALITE 9
  10. 10. Qu’est-ce qu’un modèle ?Un outil pour étudier un phénomène donné Analyse qualitative / quantitative Analyse comparativeDans l’analyse quantitative Bases de données Types de données, types de variables Outils statistiques Types de modèles Outils graphiques DAGs, représentations graphiques de modèles statistiques 10
  11. 11. Modèles d’association vs Modèles causauxY-a-t-il des associations? Y-a-t-il des relations de causalité?Contexte socio-démo-politique Contexte socio-démo-politique // connaissance d’arrière plan connaissance d’arrière planHypothèses statistiques Hypothèses statistiques Hypothèses «extra-statistiques» Hypothèses causales Méthodologie hypothetico- déductive 11
  12. 12. La segmentation de l’inférence causaleCollecte de données• Choix des variables, concepts, indicateurs• Connaissance d’arrière plan Modèle statistique général • Une famille de fonctions de distributions • Données: une réalisation du «chance set up» Modèle statistique spécifié • Modèle d’association • Description du chance set up en termes de dépendences Modèle causale • Modèle statistique «augmenté» 12
  13. 13. Que modélise-t-on ?Des phénomènes sociaux Les mécanismes qui le produisent On cherche à ouvrir la boîte noireDans quels buts? Causalité / explication Intervention Prédiction 13
  14. 14. MODELES ET VARIATION 14
  15. 15. Causalité: épistémologie vs ontologieComment établissons-nous des relations causales ? Une question épistémologique La recherche d’une « logique» (rationale)Qu’est-ce que la causalité? Une question ontologique La recherche de définitionsLes philosophes ont réfléchi bcp plus à l’ontologieÉpistémologie et méthodologie: une frontière floue 15
  16. 16. La logique de variationQuel concept guide la construction ettest du modèle ? Régularité, manipulation, invariance, …Donner à chaque concept sa bonne placedans l’analyse causale 16
  17. 17. Variation et construction du modèleY-a-t-il des co-variations significative(entre variables) ? D’après la connaisse d’arrière plan Sous forme de dépendance dans le modèle statistique spécifié 17
  18. 18. Variation et causalitéCes variations sont-elles causales ? Décomposition récursive Exogéneité Invariance RégularitéDes contraintes sur les variations dans le butd’une interprétation causale 18
  19. 19. MODELES ET MODELISATION 19
  20. 20. Modélisation: Un parapluie sur les modèlesIl faut distinguer: Un modèle particulier pour étudier les données à disposition dans un contexte donné Une stratégie de modélisation à suivre dans la construction de n’importe quel modèle 20
  21. 21. Méthodologie hypothetico-déductiveEn bref et par étapes: 1. Formulation des hypothèses 2. Construction du modèle conceptuel 3. Transformation en un modèle opérationnel 4. Test et interprétationPas de lapins causales de chapeaux statistiquesPas de confession des données après tortureDéduction au sens large 21
  22. 22. Validité et veritéValidité d’un modèle Interne, Externe, … Représentativité de l’échantillon, possibilité de répliquer l’étude «Mon modèle, tient-il la route?»Vérité d’un énoncé (causale) Un «raccourci» du problème de la validé ? 22
  23. 23. Pour conclurePluralisme méthodologique Adapter les modèles statistiques selon ses besoins Adopter une stratégie commune de modélisationLogique de variation pour la construction et test des modèles A chaque concept sa place dans l’analyse causaleSegmenter l’inférence Le passage de l’association à la causalité requiert bien plus d’une sophistication des statistiquesValidité et vérité Un modèle n’est pas vrai, tout au plus il est cohérent, solide, bref valide 23
  24. 24. Sugestions de lectureRusso F. (2009). Causality and Causal Modelling in the Social Sciences. Measuring Variations, Springer.Russo F. (2011). Correlational data, causal hypotheses, and validity. Journal for General Philosophy of Science, 42(1), 85-107.Russo F., Mouchart M., Wunsch G. (2011). Inferring causality through counterfactuals in observational studies. Some epistemological issues. Bulletin of Sociological Methodology, 111, 43-64.Mouchart M., Russo F., Wunsch G. (2010). Inferring causal relations by modelling structures. Statistica, LXX(4), 411-432.Mouchart M. and Russo F. (2011). Causal explanation: recursive decompositions and mechanisms, in Illari P., Russo F. and Williamson J. (eds), Causality in the sciences, 317-337, Oxford University Press.Wunsch G., Russo F., Mouchart M. (2010). Do we necessarily need longitudinal data to infer causal relations?, Bullettin de Methodologie Sociologique – Bullettin of Sociological Methodology, 106 (1), 5-18.Moneta A. and Russo F. On the segmentation of causal inference. In preparation. 24

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