Measuring Variations Federica Russo [email_address]
<ul><li>The law of causality, I believe, like much that passes muster amongst philosophers, is a relic of a bygone age, su...
Measuring Variations
Overview <ul><li>Causality in the natural sciences  vs   </li></ul><ul><li>Causality in the social sciences </li></ul><ul>...
Natural sciences:  The dominant paradigm <ul><li>Mechanist approaches: </li></ul><ul><ul><li>Modelling causal mechanisms <...
Causal modelling in the  social sciences <ul><li>Statistical characterization of  </li></ul><ul><li>causal relations </li>...
The rationale of causality <ul><li>An epistemological question </li></ul><ul><li>The rationale:  measuring variations </li...
Measuring variations Empirical arguments <ul><li>Mother’s Education and Child Survival </li></ul>Caldwell’s causal reasoni...
Measuring variations Methodological arguments <ul><li>Structural equations models </li></ul><ul><li>Y=  X +   </li></ul>...
Measuring variations The philosophical literature <ul><li>Suppes (1970),  A probabilistic theory of causality </li></ul><u...
Measuring variations The philosophical literature <ul><li>Woodward (2003),  Making things happen </li></ul><ul><li>Causal ...
Measuring variations Foundations <ul><li>Quetelet (1869),  Physique Sociale </li></ul><ul><ul><li>How can we identify and ...
Measuring variations Foundations <ul><li>Mill (1889),  System of Logic </li></ul><ul><ul><li>The experimental method is ba...
Measuring variations Foundations <ul><li>Durkheim (1897),  Le suicide </li></ul><ul><ul><li>A study into the  variability ...
Measuring variations Objections <ul><li>Why not invariance under intervention? </li></ul><ul><li>A restatement of Hume’s r...
Methodological Consequences <ul><li>Associational models  vs . Causal models </li></ul><ul><li>Causal models benefit of a ...
Foundational consequences: <ul><li>Social sciences aim to understand, </li></ul><ul><li>predict and intervene on society: ...
To sum up <ul><li>I quitted the dominant paradigm to look carefully into the social sciences </li></ul><ul><li>I went hunt...
To investigate further… <ul><li>Levels of causation; interpretation of probability in the social, health, public health sc...
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Kent Phil Dept March06

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  • [attention getter] Read quotation - Once you’ve read this, you might choose to defend causality or the monarchy. I’ll talk about causality…
  • [need] Causality is old and still fashionable issue both in philosophy and in the sciences. Several new accounts in philosophy, several research methods in the sciences. Nonetheless, (i) sometimes philosophy develops concepts quite independently of the scientific practice. (ii) science develops more complex models neglecting a conceptualization of notions [task] Choose a specific domanin – the social sciences (very broadly conceived) – and analyse their models. Devote to a *specific* question: what is the notion or rationale of causality involved in causal models in the social sciences? By restricting the research domain I want to make a few but clear and accurate claims about causality. [main message] I went hunting for the bottom-line concept of causality and I suggest that this notion is that of *variation*.
  • -browse on topics
  • Philosophy of causality : dominant paradigm = natural science (even hardest attack to causality – Russell’s – refers to phyisics) Leading approach nowadays is the mechanist account (Salmon-Dowe) Basic features: notion of physical process, interaction (give ex of causal process) Salmon: physical processes and interactions (even in a probabilistic sense) give us a better understanding of causality. This equally applies to natural and social sciences. Example of the Mayor  Salmon’s moral: physical processes are more fundamental than statistical relevance relations  My moral: how can we identify complex socio-political processes in terms of physical processes? In other words, I’m questioning the plausibility of the mechanist account in the social sciences.
  • Survey of the scientific literature, in particular of causal analysis in several disciplines in the social sciences. E.g.: demography, econometrics, epidemiology … (yes, it is debatable whether epidemiology is a social science – I take social sciences in a very broad sense, and insofar society, well, distribution and causes of disease in society, is concerned, epidemiology, to my eyes, falls into the social sciences) Morals: Causal relations are not characterized in terms of physical processes. Instead, statistical characterization. Use of statistical notions. Of course, problem of correlation is not causation, statistical  probabilistic characterization. Causal relations are not sharply deterministic. Not an ontological commitment to inderminism, just a problem of epistemic acces. Role of causal context. Use of background knowledge. Formulation of causal hypotheses in this context, H-D methodology to confirm. Social scientists seem to be primarily interested in testing variations among variable. This might be trivial to practising scientists, I’ll give arguments why it is not.
  • So, the question: what is the rationale of causality in causal modelling? Let me tell you immediately who is the murderer: VARIATION. And now let’s see how miss murple came to this conclusion. What ideas conveys this rationale. An epistemological question. What is a rationale. It is not a definition (metaphysical question) Note that proposing a new rationale of causality (different from the received view – based on various forms… - on regularity) has consequences on epistemology as well as metaphysics and methodology. It is a bottom-line concept. Those who don’t like circularity will be happy: variation is not a causal concept by itself. In the discussion of objections I’ll show why variation is bottom-line – why conceptually preceds other notions invoked (e.g. regularity or invariance) The case for the rationale of variation: Empirical arguments Methodological arguments Foundations Objections Methodological consequences Foundational consequences
  • Briefly present the context of Caldwell study on child mortality and mother’s education. 1979 article in Pop Studies. developing countries, in part rural &amp; urban area in Nigeria; objective= understand factors that influence child mortality; former studies: focus on sanitary, medical, social, political factors. evidence gathered in other studies. In analyzing impact of public health service, Caldwell notices that many socio-ec factors provide little expl of mortality rates. INSTEAD, mother’s education is of surprising importance  design a study focused on mother’s ed. Briefly explain methodology employed Analysis and comparison of contigency tables. Those tables show how frequencies of some factors vary depending on other factors. Discuss quotation  Caldwell’s causal reasoning
  • Briefly explain the meaning of the structural equation. What is Y, what X, what  . The linear relation, equality is not algebraic equality … interpretation: variations in X accompany variations in Y.  quantify those variations. So, variation is the primary concept used in this equation representing a causal relation. since variation is not a causal concept, what guarantees the causal intepretation  wait and see the methodological consequences.
  • PT in philosophy have been developed in slightly different ways by different authors. Present Suppes (i) pioneer but more accessible than I.J. Good (ii) bcz in a way or another every current theory refers to Suppes’ (iii) bcz we just need to grasp the basic intuition behind and not deal with all criticisms, objections etc 2 temporally distinct events, event C preceded event E; we can attach probabilities to them, and a cause is an event such that P(E|C)&gt; P(E); Suppes aware of possibility that events lower (not increase) prob (negative causes, preventatives)  P(E|C) &lt; P(E). In general P(E|C)  P(E) compare marginal and conditional probability check whether the marginal probability differs from conditional  the cause is responsible for the variation.
  • Theory of causal explanation. Causes explain effects because they make effects happen. Causal generalizations are change-relating or variation-relating of course, problem of distinguishing causal from spurious  change-relating relations have to show a certain invariability as prescribed by invariance condition in structural models. Shall see that invariance is not bottom-line concept.
  • Foundations. I show that forefathers of quantitative analysis in the social sciences, and also Mill (renowned methodologist of the experimental method) rely on the notion of variation – without fully conceptualizing its importance for causality Start from Quetelet. objective of his work is to study the causes that operate on the development of man, in particular the goal is to measure their influence and their mode of reciprocal action. explaining, quite informally, how he intends to detect and measure the causes that influence his average man . Basically, what he describes is the comparative method as Mill will elaborate in detail. Quetelet’s causal reasoning is impregnated of the variation rationale because his search for causes of human development starts with the observation of a wide variability in the mortality tables he calculated . And then looks for factors that are responsible of those variations. Causal factors are detected by testing variations in mortality tables.
  • In the System of logic the experimental inquiry is seen as the solution to problem of what process for ascertaining what phenomena are related to each other as causes and effects. We have, says Mill, to follow the Baconian rule of varying the circumstances, and for this purpose we may have recourse to observation and experiment. In this general idea of varying the circumstances the variation rationale is clearly already at work. The experimental inquiry is basically composed of four methods: 1. Method of agreement (comparing together different instances in which the phenomenon occurs), 2. method of difference (comparing instances in which the phenomenon does occur with instances in other respects similar in which it does not), joint method of agreement and difference (that consists in a double employment of the method of agreement, each proof being independent of the other, and corroborating it), 3. method of residues (subducting from any given phenomenon all the portions which can be assigned to known causes, the remainder will be the effect of the antecedents which had been overlooked or of which the effect was as yet an unknown quantity), 4. method of concomitant variation is particularly useful in case none of the preceding methods (agreement, difference, residues) is able to effect a variation of circumstances. For instance in presence of permanent causes or indestructible natural agents which is impossible to either to exclude or to isolate, which we can neither hinder from being present nor contrive that they shall be present alone. OSS: that the experimental method inapplicable in the social sciences. Interestingly, Durkheim goes against this view (les Règles de la methode sociologique, ch VI), in particular he maintains that the method of concomitant variation is fruitfully used in sociology.
  • suicide as a social phenomenon ; searches for the social causes of suicide, namely for the factors depending on which the social rate of suicide varies . study the variability across time of the suicide rate (= ratio between number of voluntary deaths on the overall population). This variability is quite insignificant across time within the same population, but it is instead considerable across different societies. - The rationale of variation permeates Durkheim’s causal reasoning about suicide as a social phenomenon, because by examining how the suicide ratio varies across societies he aims at detecting the social factors this variation depends on. The variation rationale in Le suicide is extrapolated from his argumentation along the essay. However, the rationale is definitively explicit in Les règles de la méthode sociologique . - The comparative method is, according to Durkheim the only way to make sociology scientific. However, not all procedures of the comparative method will do. Only the Millian method of concomitant variations will. Durkheim has, nonetheless a determinist conception of the causal relation, for he believes in the principle “same cause, same effect”; the comparative method is scientific, i.e. conformed to the principle of causality, only if comparisons are analyzed under the supposition that to the same effect always corresponds the same cause (1912 : 157). In Durkheim a probabilistic characterization of causal relations is totally absent, and if one of the two phenomena occurs without the other, this might be due to the effect of a third phenomenon operating against the cause or to the fact that the cause is present under a different forms. Durkheim of course is aware of the fact that a concomitant variation might be due to a third phenomenon acting as a common cause or as an intervening factor between the first and the second. He concludes that the results of the method of concomitant variation are therefore to be interpreted. If a direct link from the cause to the effect is not self evident, then the mechanism responsible for the concomitant variation has to be unveiled in order to rule out a case of common cause Interestingly, in Durkheim we can also find an “invariance condition” ante litteram . What we have to compare is not isolated variations but series of variations, regularly constituted (1912 : 165).
  • First objection: Come from recent philosophical accounts (Woodward, Hausman …) and practising scientists using structural equations models: causality is invariance under intervention. Second objection from philosophers who see in this emphasis on variation just a restatement of Hume’s regularist account. Third objection: if the population is too homogenous we won’t detect any variation. EX of effects of Muslim culture on gender equality in a Muslim culture Answers. - Invariance and regularity are not the bottom-line concepts: “… of what?” So, what role do invariance and regularity play?  constraints, NOT rationale. Variation conceptually precedes invariance and regularity – it is what we look for in the first place, it is what we put constraints on. The case of the homogenous populations exactly show that we detect causal relations by looking for and testing variations.
  • Here we come back to the issue anticipated before: if variation is not per se a causal concept, what guarantees the causal intepretation? Main idea: distinguish associational models – causal models. What associational models do, what they are constituted of. What causal models do, what they have more than associational models. Explain features.
  • Foundational consequences. Question: why should we bravely defend causality? Still work on definition, rationale, methods? Is it just a fashionable topic for academics? NO  goals of social sciences, cognitive and practical. Both need causation.
  • Kent Phil Dept March06

    1. 1. Measuring Variations Federica Russo [email_address]
    2. 2. <ul><li>The law of causality, I believe, like much that passes muster amongst philosophers, is a relic of a bygone age, surviving, like the monarchy, only because it is erroneously supposed to do no harm. </li></ul><ul><li>Bertrand Russell, On the notion of cause , 1913 </li></ul>
    3. 3. Measuring Variations
    4. 4. Overview <ul><li>Causality in the natural sciences vs </li></ul><ul><li>Causality in the social sciences </li></ul><ul><li>The rationale of causality: measuring variations </li></ul><ul><ul><li>Empirical arguments </li></ul></ul><ul><ul><li>Methodological arguments </li></ul></ul><ul><ul><li>The philosophical literature </li></ul></ul><ul><ul><li>Foundations and objections </li></ul></ul><ul><li>Methodological consquences </li></ul><ul><ul><li>Associational models vs causal models </li></ul></ul><ul><li>Foundational consequences </li></ul><ul><ul><li>The case for a causalist perspective </li></ul></ul>
    5. 5. Natural sciences: The dominant paradigm <ul><li>Mechanist approaches: </li></ul><ul><ul><li>Modelling causal mechanisms </li></ul></ul><ul><ul><li>with physical processes </li></ul></ul>Is this a viable approach in the social sciences?
    6. 6. Causal modelling in the social sciences <ul><li>Statistical characterization of </li></ul><ul><li>causal relations </li></ul><ul><li>Causal claims in causal contexts </li></ul><ul><li>Measuring variations among variables </li></ul>
    7. 7. The rationale of causality <ul><li>An epistemological question </li></ul><ul><li>The rationale: measuring variations </li></ul><ul><li>Variation: the bottom-line concept </li></ul>
    8. 8. Measuring variations Empirical arguments <ul><li>Mother’s Education and Child Survival </li></ul>Caldwell’s causal reasoning: “ If any one of these environmental influences had wholly explained child mortality, and if female education had been merely a proxy for them, the CM [child mortality] index would not have varied with maternal education in that line of the table. This is clearly far from being the case.”
    9. 9. Measuring variations Methodological arguments <ul><li>Structural equations models </li></ul><ul><li>Y=  X +  </li></ul><ul><li>Variations in X accompany variations in Y </li></ul><ul><li> Quantifies those variations </li></ul>
    10. 10. Measuring variations The philosophical literature <ul><li>Suppes (1970), A probabilistic theory of causality </li></ul><ul><li>P(Effect|Cause)  P(Effect) </li></ul><ul><li>The cause is responsible for a variation </li></ul><ul><li>in the marginal-conditional probabilities </li></ul>
    11. 11. Measuring variations The philosophical literature <ul><li>Woodward (2003), Making things happen </li></ul><ul><li>Causal generalizations are change-relating . </li></ul>
    12. 12. Measuring variations Foundations <ul><li>Quetelet (1869), Physique Sociale </li></ul><ul><ul><li>How can we identify and measure the </li></ul></ul><ul><ul><li>causes that influence the average man? </li></ul></ul><ul><ul><li>The comparative method: Mill ante litteram </li></ul></ul><ul><ul><li>Working on variations in the mortality tables </li></ul></ul>
    13. 13. Measuring variations Foundations <ul><li>Mill (1889), System of Logic </li></ul><ul><ul><li>The experimental method is based on the </li></ul></ul><ul><ul><li>Baconian rule of varying the circumstances </li></ul></ul><ul><ul><li>The four methods </li></ul></ul><ul><ul><li>Agreement, Difference, Residues, Concomitant Variations </li></ul></ul><ul><ul><li>Are all based on the evaluation of variations </li></ul></ul>
    14. 14. Measuring variations Foundations <ul><li>Durkheim (1897), Le suicide </li></ul><ul><ul><li>A study into the variability of suicide rate. </li></ul></ul><ul><ul><li>A search for the causes depending </li></ul></ul><ul><ul><li>on which the suicide rate varies. </li></ul></ul><ul><li>Durkheim (1885), Les règles de la méthode sociologique </li></ul><ul><ul><li>The method of concomitant variations </li></ul></ul><ul><ul><li>makes sociology scientific. </li></ul></ul>
    15. 15. Measuring variations Objections <ul><li>Why not invariance under intervention? </li></ul><ul><li>A restatement of Hume’s regularist account? </li></ul><ul><li>What if the population is too homogenous? </li></ul><ul><ul><li>Invariance and regularity are constraints </li></ul></ul><ul><ul><li>imposed on the causal relation </li></ul></ul><ul><ul><li>Variation conceptually precedes </li></ul></ul><ul><ul><li>invariance and regularity </li></ul></ul><ul><ul><li>Homogeneity is not an objection – </li></ul></ul><ul><ul><li>it shows that we look for variations to be tested </li></ul></ul>
    16. 16. Methodological Consequences <ul><li>Associational models vs . Causal models </li></ul><ul><li>Causal models benefit of a richer apparatus: </li></ul><ul><ul><li>Background knowledge </li></ul></ul><ul><ul><li>Statistical hypotheses </li></ul></ul><ul><ul><li>Causal hypotheses </li></ul></ul><ul><ul><li>Hypothetico-deductive methodology </li></ul></ul>
    17. 17. Foundational consequences: <ul><li>Social sciences aim to understand, </li></ul><ul><li>predict and intervene on society: </li></ul><ul><ul><li>Cognitive goal: gain causal knowledge </li></ul></ul><ul><ul><li>Practical goal: inform social policy, intervene on individuals’ behaviour </li></ul></ul>The case for a causalist perspective
    18. 18. To sum up <ul><li>I quitted the dominant paradigm to look carefully into the social sciences </li></ul><ul><li>I went hunting for the rationale of causality </li></ul><ul><li>in causal models </li></ul><ul><li>Social science practice, methodology, philosophy, and forefathers of quantitative analysis point to: </li></ul><ul><li>MEASURING VARIATIONS </li></ul>
    19. 19. To investigate further… <ul><li>Levels of causation; interpretation of probability in the social, health, public health sciences; explanation; external validity; background knowledge; theory of modelling; ontology of social objects; … </li></ul>

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