Empirical Generalisations Kent Nov07
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Empirical Generalisations Kent Nov07

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Empirical Generalisations Kent Nov07 Presentation Transcript

  • 1. Empirical Generalisations in Social Science Federica Russo Institut Sup érieur de Philosophie Université catholique de Louvain
  • 2. Overview
    • The question:
    • are there laws in social science?
    • Relevance of the question
      • Historically
      • In the current debate
    • The consensus and the challenge
    • Empirical generalisations
    • in structural models
    • On invariance:
    • Pandora’s box is open
  • 3. Relevance of the question
    • Quetelet (1869):
    • society is reg imented by laws
    • as much as Nature
      • Statistics is a science of the general,
      • it establishes laws by analysing regularities
      • The Average Man, i.e. the mean
      • around which social elements oscillate,
      • is the basis of a social physics
  • 4. Quetelet’s reading shows
    • The goal of finding laws of society has fairly long history
    • “ Laws” in a strong sense, not just Humean regularities
    However
    • Quetelet is liable to objections, e.g.:
      • Laws can’t be established by investigating regularities,
      • but, if at all, by investigating variations
      • Statistics is not the study of the mean
      • but the study of the variance
  • 5. Relevance of the question
    • The debate is still open
      • The social sciences cannot establish laws
      • because they are not as mature
      • as the natural sciences
      • They are mature but there aren’t
      • any laws to discover
      • There are laws, but we cannot know them
      • If there are laws, it is unclear what
      • kind of entities and mechanisms are involved
  • 6. Consider for instance:
    • 1) Roberts 2004
      • Laws are universal regularities
      • The special sciences
      • do not have such laws
      • The absence of laws points to
      • an essential difference between
      • the natural and social sciences
  • 7. Consider for instance:
    • 2) Kinkaid 2004
      • Some laws of physics do not establish
      • universal generalisations
      • but causal mechanisms
      • Such laws describe general tendencies,
      • sometimes fragile
      • Many laws in social science are of this type
  • 8. Consider for instance:
    • 3) Woodward and Hitchcock 2003
      • Laws are empirical generalisations having
      • the characteristic of being invariant
      • Invariance gives them
      • explanatory and predictive power
  • 9.
    • An implicit consensus
      • If there are laws, they don’t have the same characteristics of the laws of physics
    • Whence the question
      • What are they?
    • Answer
      • Empirical generalisations
    A weaker concept A different concept
  • 10. The challenge
    • To give an account of
    • empirical generalisations that is
    • Reasonable Meaningful Useful
  • 11. The strategy
    • What is an empirical generalisation
    • in social science ?
    Goals of social science Cognitive Action-oriented Role of causal knowledge Structural modelling: Establishing empirical generalisations Their characteristics will depend on the conditiions of structural models
  • 12. Goals
    • Cognitive
      • Understand/explaining
      • social phenomena
    • Action-oriented
      • Inform/direct social policies
  • 13. Role of causal knowledge
    • Cognitive aspect
      • Beyond description,
      • to provide foundations for action
    • Action-oriented aspect
      • It presupposes intervening on
      • causal relationships/mechanisms
    • How to acquire such
    • causal knowledge?
  • 14. Structural modelling, the quantitative approach  54  4  13  34  12  2 X 1 Economic development X 2 Social development X 3 Sanitary infrastructures X 4 Use of sanitary infrastructures X 5 Age structure Y Mortality
  • 15. Elements
    • Assumptions
      • Statistical
      • Extra-statistical
      • Causal
    • Methodology
      • Hypothetico-deductivism
    • Key notions
      • Background knowledge
      • Exogeneity
      • Invariance
  • 16. In more detail: H-D methodology
    • 1) formulate the hypothesis
    • 2) build the model
    • 3) confirm/disconfirm the hypothesis
    • Note:
      • Not exactly Popperian H-D
      • Terminological problems
    • H-D methodology makes
    • structural models flexible
  • 17. In more detail: background knowledge
    • General knowledge of the
    • socio-political context
    • Similar evidence of the same causal
    • mechanism in other populations
    • Knowledge of the
    • physical-biological-physiological mechanism
    • Use of similar/different methods
    • and/or of data
  • 18. In more detail: exogeneity
    • An exogenous variable:
    • “ its” mechanism does not influence
    • the mechanism of interest
    • In a structural model,
    • an exogenous variable is
    • a causal variable
  • 19.  54  4  13  34  12  2 X 1 Economic development X 2 Social development X 3 Sanitary infrastructures X 4 Use of sanitary infrastructures X 5 Age structure Y Mortality
  • 20. In more detail: invariance
    • The traditional definition:
      • Causality requires “invariance under
      • intervention”, i.e. a relation has to be
      • invariant under a large class of
      • interventions or environmental changes
    • The test
      • invariance require setting up
      • different initial conditions
    • A counterfactual characterisation
  • 21. In more detail : invariance
    • Woodward & Hitchcock 2003:
      • A relationship R between variables X and Y is invariant if it would continue to be true (or approximately true) in at least some hypothetical situations or possible worlds in which the value of X is changed as the result of an intervention. That is, there must be some non-actual value x of X such that the following counterfactual is true: ‘if X were equal to x, then the values of X and Y would stand (approximately) in the relation R.’
  • 22. In more detail: invariance
    • Distinguish:
      • Invariance  concerning the variables
      • Structural stability  concerning the model
    • Invariance is a property of observations,
    • not of the model
    • The test:
      • parameters have the same value or at least
      • the same sign across sub-samples of the data base
    • We get out of counterfactuals
  • 23. In more detail: structural stability
    • A model is structurally stable if
      • The causal variables are exogenous
      • Relations among variables are invariant
      • Background knowledge backs up
        • exogeneity, invariance and the structure
    • Distinguish
      • Internal vs external stability
  • 24. Structural – what does it mean?
    • Looking for structures, mechanisms
    • A special case of the
    • general statistical model
    • An umbrella for different types
    • of causal models
    • Qualitative analysis is also structural
  • 25. That’s all to say
    • Structural models establish
    • empirical generalisations
    • A causal claim that state an invariant
    • relation in structural model
    • Empirical generalisations  causal
    • Summary of statistics  descriptive
    • Empirical generalisations allow
    • explanation – prediction – intervention
    • because they are the result of a structural model
  • 26. What’s new, then?
  • 27. Woodward’s invariance
    • Goal
      • Defend a theory of explanation
      • and of explanatory generalisations
    • The claim
      • Empirical generalisations are
      • explanatory because invariant
    • The scope
      • Explanation in the special sciences,
      • social and natural
  • 28. The divergence
    • Woodward’s central idea
      • Empirical generalisations show patterns of counterfactual dependence
      • Their explanatory power is due to their being able to answer WITHBD-questions
    • Counterfactuality is central to
      • Invariance
      • Explanation
      • Causal modelling
  • 29. Deeper and deeper divergences
    • Invariance-based approaches and
    • in general counterfactual approaches
    • claim that they will establish
    • causal relations by evaluating
    • effects of interventions
  • 30. Pandora’s box is open
    • Woodward’s invariance presupposes
    • an experimentalist approach
    • But what do we do in social science
    • with observational data?
  • 31. The counter-objection
    • 1) Interventions do not have an anthropomorphic characterisation
    • OK, fine
    • 2) If we cannot intervene, we consider a hypothetical experiment
    • Pandora’s box is wide open
  • 32. In extremis rescue?
    • Woodward 2003:
      • Instead, the role of [interventions] is to serve as a regulative idea: they tell us what must be true of the relationship between X and Y if X causes Y and in this way tell us what we should aim at establishing, perhaps on the basis of an imperfect or nonideal experiment,
      • if we want to show that a causal claim is true.
    • But that’s exactly the problem!
  • 33. To sum up
    • Are there laws in social science?
      • The question is relevant
      • We’d better look into the
      • concept of empirical generalisation
      • I’ve done that through an
      • analysis of structural modelling
      • The divergence with the “received views”
      • opened a Pandora’s box
  • 34. Some remarks
    • Partisans of counterfactual approaches
    • oppose partisans of decision theory
    • Opposition is due to the weak foundations
    • of the counterfactual approach
    • Either we get rid of counterfactuals or
    • we provide them with better foundations
    • But mostly, counterfactuals do not say
    • how to draw causal conclusions
    • from observational data
  • 35. To conclude
    • Are there laws in social science?
      • Perhaps, but right now we have
      • but empirical generalisations,
      • that is causal statements that claim an invariant relation in a structural model