Empirical Generalisations Kent Nov07

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  • X1 et X5 sont exogenes – commenter
  • Empirical Generalisations Kent Nov07

    1. 1. Empirical Generalisations in Social Science Federica Russo Institut Sup érieur de Philosophie Université catholique de Louvain
    2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 26. What’s new, then?
    27. 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. 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. 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. 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. 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. 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. 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. 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. 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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