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Csf Russo Measuring Variations


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Csf Russo Measuring Variations

  1. 1. Measuring variations Causality and causal modelling in the social sciences Federica Russo Philosophy, Louvain & Kent
  2. 2. Overview <ul><li>Locate this work: </li></ul><ul><ul><li>Metaphysics, epistemology, </li></ul></ul><ul><ul><li>methodology of causality </li></ul></ul><ul><ul><li>Domain; interest; objective </li></ul></ul><ul><li>The guiding question </li></ul><ul><ul><li>Rationale (vs. definition) </li></ul></ul><ul><li>Methodology of research and </li></ul><ul><li>types of arguments </li></ul><ul><li>A taste of methodological arguments </li></ul><ul><ul><li>Structural equations </li></ul></ul><ul><li>A taste of possible objections </li></ul><ul><ul><li>Regularity; Invariance; Homogenous populations </li></ul></ul>
  3. 3. Philosophy of causality <ul><li>Metaphysics </li></ul><ul><ul><li>What causality/cause is </li></ul></ul><ul><li>Epistemology </li></ul><ul><ul><li>How do we know about causal relations </li></ul></ul><ul><li>Methodology </li></ul><ul><ul><li>Develop/implement methods </li></ul></ul><ul><ul><li>for discovery/confirmation of causal relations </li></ul></ul>
  4. 4. This work <ul><li>Epistemology of causality </li></ul><ul><li>Domain </li></ul><ul><ul><li>quantitative social science </li></ul></ul><ul><li>Interest </li></ul><ul><ul><li>causal reasoning in causal modelling </li></ul></ul><ul><li>Objective </li></ul><ul><ul><li>dig out a neglected notion </li></ul></ul><ul><ul><li>in the philosophy of causality: variation </li></ul></ul>
  5. 5. The guiding question <ul><li>When we reason about cause-effect </li></ul><ul><li>relations in causal modelling, </li></ul><ul><li>what notion guides this reasoning? </li></ul><ul><li>Regularity? Invariance? Production? ... </li></ul><ul><li>Hunting for a rationale </li></ul>
  6. 6. Rationale vs. definition <ul><li>Rationale: </li></ul><ul><ul><li>a principle/notion/concept underlying </li></ul></ul><ul><ul><li>decision/reasoning/modelling </li></ul></ul><ul><li>Definition: </li></ul><ul><ul><li>A description of a thing by means </li></ul></ul><ul><ul><li>of its properties or if its function </li></ul></ul><ul><li>Here: </li></ul><ul><ul><li>hunt for the notion underlying model building </li></ul></ul><ul><ul><li>and model testing: rationale, not definition </li></ul></ul>
  7. 7. Methodology of research <ul><li>Bottom-up rather than top-down </li></ul><ul><li>A philosophical investigation that </li></ul><ul><ul><li>starts from the scientific practice, </li></ul></ul><ul><ul><li>within the scientific practice raises </li></ul></ul><ul><ul><li>methodological and epistemological issues, </li></ul></ul><ul><ul><li>for the scientific practice points </li></ul></ul><ul><ul><li>to the path forward </li></ul></ul>
  8. 8. The answer <ul><li>Causal modelling is regimented by </li></ul><ul><li>a rationale of variation </li></ul>
  9. 9. Arguments <ul><li>Empirical: </li></ul><ul><ul><li>Look at informal reasoning in case studies </li></ul></ul><ul><li>Methodological: </li></ul><ul><ul><li>Look at rationale of model building & testing </li></ul></ul><ul><ul><li>in various causal models </li></ul></ul><ul><li>Philosophical: </li></ul><ul><ul><li>Look at arguments given by other philosophers </li></ul></ul><ul><li>Foundational: </li></ul><ul><ul><li>Look at forefathers of causal modelling </li></ul></ul><ul><li>Compatibility: </li></ul><ul><ul><li>Look at various established philosophical accounts </li></ul></ul>
  10. 10. A taste of methodological arguments <ul><li>Consider a structural equation </li></ul><ul><li>Y =  X+  </li></ul><ul><ul><li>Are there meaningful co-variations between X and Y? </li></ul></ul><ul><ul><li>Are those variations chancy or causal? </li></ul></ul><ul><ul><ul><li>hypothesis testing; invariance; exogeneity </li></ul></ul></ul>
  11. 11. Therefore… <ul><li>Variation is a pre condition </li></ul><ul><li>with respect to other notions </li></ul><ul><ul><li>E.g.: regularity, invariance </li></ul></ul><ul><li>Any role left to those? Yes – constraints : </li></ul><ul><ul><li>Regularity: often enough </li></ul></ul><ul><ul><li>Invariance: stability of parameters </li></ul></ul><ul><li>Rule out accidental and spurious variations, </li></ul><ul><li>Grant causal interpretation of variations </li></ul>
  12. 12. A taste of objections <ul><li>Regularity </li></ul><ul><ul><li>Mine is just a reformulation of regularity theory </li></ul></ul><ul><ul><ul><li>Only partly true </li></ul></ul></ul><ul><ul><li>Regularity is more basic. </li></ul></ul><ul><ul><ul><li>Not quite: regularity of what? </li></ul></ul></ul><ul><li>Invariance </li></ul><ul><ul><li>Invariance is more basic. </li></ul></ul><ul><ul><ul><li>Not quite: invariance of what? </li></ul></ul></ul><ul><li>Homogenous populations </li></ul><ul><ul><li>No variations in homogenous populations. </li></ul></ul><ul><ul><ul><li>That’s the point: to make variations emerge </li></ul></ul></ul>
  13. 13. Want to know more?