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Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
Empirical Generalisations Kent Nov07
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Empirical Generalisations Kent Nov07

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  • X1 et X5 sont exogenes – commenter
  • Transcript

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

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