On empirical generalisations

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  • MotivationTo law or not to law. That is the questionFrom physics. We have laws, let’s quarrel on what they are.From biology. It is uncertain that we have laws. Let’s quarrel on whether there are / will / should be any. If so what they are and whether they are (not) the same as physics laws.From the special sciences. Almost certain there aren’t any. Is there any chance they can explain anything at all?Woodward’s solutionYes they can. We don’t need laws. Empirical generalisations do the job. (Let’s specify the characteristics making them causal / explanatory)NeedAssessment of empirical generalisationsMain messageThe current account, based on manipulation, faces troubles
  • On empirical generalisations

    1. 1. On empirical generalisations<br />Federica Russo<br />Philosophy, Kent<br />
    2. 2. Overview<br />Empirical generalisations and causal assessment:<br />the manipulationist account<br />The dilemma about manipulationism<br />1. A conceptual analysis of causation<br />2. A methodological account of causal assessment<br />(a) Strictly interpreted<br />(b) Charitably interpreted<br />Empirical generalisations and causal assessment<br />without manipulation<br />2<br />
    3. 3. Manipulationism<br />Information about the results of interventions<br />is of utmost importance<br />for explanation or causal assessment<br />3<br />
    4. 4. Causal assessment<br />Empirical generalisations<br />Change-relating relations between variables<br />Spurious? Accidental?<br />Invariance<br />Empirical generalisations must show some invariability<br />in order to be causal<br />Intervention <br />Empirical generalisations must be invariant<br />under specified interventions on the cause<br />4<br />
    5. 5. For instance …<br />… in physics:<br />Ideal gas law is invariant under a whole range<br />of interventions on temperature<br />… in biology:<br />The relation between (fictious) gene R and ability<br />to learn and read is not very stable under modification<br />of e.g. schooling or culture<br />5<br />
    6. 6. Manipulationismis trapped in a dilemma<br />6<br />
    7. 7. Conceptual manipulationism<br />The dilemma – Horn 1<br />7<br />
    8. 8. Identity conditions:<br />X causes Y if, and only if, manipulations on X<br />accordingly yield changes on Y<br />Manipulation is the concept cashing out causation<br />Unilluminating as for the methods for causal assessment<br />To regain coherence of concept and methods:<br />Manipulationist methodology <br />Horn 2<br />8<br />
    9. 9. Methodological manipulationsim<br />The dilemma – Horn 2<br />9<br />
    10. 10. A method for causal assessment:<br />Were manipulations on X yield changes on Y,<br />then we’d be entitled to infer that X causes Y<br />Another dilemma:<br />Methodological manipulationism can be<br />(a) Strictly interpreted<br />(b) Charitably interpreted<br />10<br />
    11. 11. (a) Strictmethodological manipulationism<br />To know whether X causes Y:<br />1. perform an intervention on X<br />2. hold fixed anything else<br />3. see what happens to Y<br />Typical situation: the controlled experiment <br />Manipulation is a tool to establish causal relations<br />But what if we cannot intervene?<br />E.g., social science, astronomy, … ?<br />11<br />
    12. 12. The manipulationist rebuts:<br />You don’t have to actually intervene:<br />idealmanipulationswill do<br />Not quite:<br />1. Some ideal interventions don’t make physical sense<br />2. Some ideal interventions cannot be tested<br />Were we to intervene: a conceptual analysis<br />Stuck back into Horn 1<br />12<br />
    13. 13. (b) Charitablemethodological manipulationism<br />Any sorts of manipulation will do: the agent’s or Nature’s.<br />If Nature manipulates, causal assessment is about evaluating variations in Y due to variations in X<br />Once Nature manipulates, ourtools for causal assessment cannot involve manipulation<br /> Back into Horn (a)<br />2. Manipulation is disingenuously taken as the rationale underpinning causal methods (experimental and non-)<br /> See later the reassessment <br />13<br />
    14. 14. Recap<br />Horn 1. A conceptual analysis of causation<br />Unilluminating as to the methods<br />Horn 2. A methodological account of causal assessment<br />a) Strictly interpreted<br />Stuck back into Horn 1<br />b) Charitably interpreted<br />Stuck back into Horn (a) <br />Disingenuous rationale of causal assessment<br />14<br />
    15. 15. Empirical generalisationsreassessed<br />
    16. 16. The core of agreement<br />Empirical generalisations reassessed<br />16<br />
    17. 17. Empirical generalisations are change-relating relations between variables<br />Change-relating reflects variational epistemology<br />To be causal, they have to be invariant<br />Not necessarily involving manipulation<br />17<br />
    18. 18. Variational epistemology<br />Empirical generalisations reassessed<br />18<br />
    19. 19. Identity conditions – conceptual analysis<br />Conditions under which a causal claim is true<br />‘X causes Y’ iff were we to manipulate …<br />Rationale – epistemology/methodology<br />Notion underlying causal reasoning/methods<br />Are there joint variations between X and Y?<br />Are those variations spurious / invariant / regular /<br />due to intervention on X …?<br />19<br />
    20. 20. invariance<br />Empirical generalisations reassessed<br />20<br />
    21. 21. Invariance doesn’t necessarily require interventions<br />In absence of manipulation:<br />Stability of X-Y relation across chosen partitions of data set, or across different populations<br />Manipulation is not a necessarytool<br />for causal assessment<br />21<br />
    22. 22. To sum up and conclude<br />Causal assessment in manipulationism<br />Empirical generalisations are invariant under manipulations of the cause<br />Manipulationism is trapped in a dilemma<br />Conceptual – lacks methodology part<br />Methodological – too strict or misleading<br />The dilemmadissolves reassessing empirical generalisations<br />Variational epistemology<br />Non-manipulationistinvariance<br />Manipulations<br />are not the building block of causal assessment<br />are a good tool, when they can be performed<br />22<br />
    23. 23. Extras<br />
    24. 24. Causal modelling<br />Y = X+<br />Variational reading<br />Variations in Y are accompanied by variations in X<br />May be just observational. Impose further constraints<br />Manipulationist reading (derived)<br />Manipulations on X make X vary such that Y varies accordingly<br />Joint variations between X-Y are due to manipulations<br />Counterfactual reading (derived)<br />Were we to vary X, Y would accordingly vary<br />Joint variations between X-Y are hypothetical<br />24<br />
    25. 25. On policy interventions<br />Policy interventions are based on causal story:<br />We manipulate because we know that X causes Y<br />Policy intervention do not establish thatX causes Y<br />Although they may lead to revise causal knowledge<br /> If the participants in policy making can at least approximate goal consensus, then the next thing they must do is to understand the causal theory that underlies the policy to be implemented.<br /> A causal theory is a theory about what causes the problem and what intervention (i.e. what policy response to the problem) would alleviate that problem. Without a good causal theory it is unlikely that a policy design will be able to deliver the desired outcome. <br />Birkland, An introduction to the policy process, 2010<br />25<br />
    26. 26. Manipulation and policy interventions<br />Some form of manipulation occurs in the special sciences too: policy interventions<br />However<br />Policy interventions are designed based on empirical generalisations validated (typically) without interventions<br />Results of policy interventions may lead to further confirmation or to question the validity of the empirical generalization<br />

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