Russo a coruna-causal interpretation

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On the causal interpretation of statistical models. Joint work with Alessio Moneta

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  • Russo a coruna-causal interpretation

    1. 1. On the causal interpretation of statistical models in social research<br />Federica Russo<br />Philosophy, Kent<br />joint work with<br />Alessio Moneta<br />Max Plank Institute, Jena<br />
    2. 2. Overview<br />Background<br />The unbearable lightness of causality<br />Associational vs Causal Models<br />Statistical vs Causal information<br />Interpreting a statistical model causally<br />The epistemic stance<br />2<br />
    3. 3. The dawn of historyof causal modelling<br />Staunch causalists<br />Quetelet, Durkheim, Wright …, Blalock, Duncan, …<br />Moderate skeptics<br />Pearl, Heckman, Hoover, …<br />… and the evergreen question:<br />When and how can we draw<br />causal conclusions from statistics?<br />3<br />
    4. 4. Intuitions and insights<br />4<br />
    5. 5. 5<br />
    6. 6. Information<br />Statistical<br />A summary of data<br />Inferential statistics<br />(sample to population)<br />Adequate and parsimonious description of the phenomenon<br />Statistical dependence<br />Causal<br />Opening the ‘black box’<br />From association to causation<br />Statistical information<br />to provide the formalised<br />empirical evidence<br />Background ‘constraints’<br />Tests<br />6<br />
    7. 7. All nice but …<br />A vicious circle introduced?<br />Not quite … <br />How much background knowledge?<br />Just the right amount …<br />7<br />
    8. 8. What’s interpretinga statistical model causally?<br />
    9. 9. The philosophers’ huntfor truthmakers<br />… that is, what makes a causal claim true<br />Difference-makers<br />Probabilistic, counterfactual, manipulation<br />Mechanisms<br />9<br />
    10. 10. Anything wrong with the hunt?<br />Conceptual analysis in philosophy of causality<br />What explicates the concept of ‘causality’<br />What makes causal claims true<br />What is causality, metaphysically<br />Conceptual analysts<br />failed to distinguish between evidence and concept<br />lost on the way epistemic practices <br />10<br />
    11. 11. What’s interpretinga statistical model causally?<br />An epistemic activity …<br />
    12. 12. In the footprints of epistemic theorists<br />Evidence and concept<br />Evidential pluralism:<br />difference-making and mechanistic considerations<br />Conceptual monism:<br />causation is an inferential map<br />Causality:<br />an epistemic category to interpret the world<br />rather than<br />a physical relation in our ontology<br />12<br />
    13. 13. Interpreting in causal terms …<br />… is deciding whether a model is valid or not<br />Making successful inferences<br />Not merely dependent on<br />the physical existence of mechanisms<br />Mechanisms have explanatory import<br />Mechanistic and difference-making<br />evidential components are tangled<br />13<br />
    14. 14. The causal interpretation is model-dependent<br />Causal conclusions depend on<br />the statistical information and machinery<br />from which they are inferred<br />Not a bad thing after all<br />Causation is not a ‘all or nothing’ affair<br />Nor a ‘once and for all’ affair<br />14<br />
    15. 15. To sum up<br />The causal interpretation of statistical model:<br />An evergreen question from the staunch causalists<br />to the moderate skeptics<br />Methodological arguments:<br />Associational vs Causal Models<br />Statistical vs Causal Information<br />Philosophical arguments<br />Against the hunt for truthmakers<br />For an ‘epistemic’ stance<br />15<br />
    16. 16. To conclude<br />We gain a lot (arguably, hopefully)<br />We don’t hamper in an endless hunt<br />for mechanisms and for difference-makers<br />Statistical and causal inferences are distinct<br />and must be kept separated<br />We acknowledge the large role played by<br />the ‘elaboration of the mind’ (Durkheim)<br />16<br />

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