Causalanalysis Systemics

481 views
407 views

Published on

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
481
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Stability/invariance correspond to theoretical saturation or informational redundancy in qualitative analysis
  • knowledge of the causal context is given by knowledge of the political, economic and social situation in Spain over the decades 1970-1980, which led to a low mortality rate at the time of the study. Knowledge of the socio-political context makes clearer the modelling strategy of this study. In fact, previous studies in demography and medical geography examined the incidence of the health system on regional mortality coming to the conclusion that regional differences in mortality could not possibly be explained by regional differences in the health system. However, Spain met deep socio-economic changes in the mid-Seventies, and consequently policy in that period simultaneously tried to intervene on improving the social and economic situation. mortality is influenced by the health system which is in turn influenced by the social and economic development. It is this background that explains the choice of distinguishing the supply and demand of medical care, unlike the majority of similar ecological studies.
  • Causalanalysis Systemics

    1. 1. Causal analysis and system analysis: complementarity or opposition? Federica Russo Philosophy, Louvain & Kent
    2. 2. Overview <ul><li>Causal analysis </li></ul><ul><ul><li>Methodology </li></ul></ul><ul><ul><li>Presuppositions </li></ul></ul><ul><li>System analysis </li></ul><ul><ul><li>Presuppositions </li></ul></ul><ul><ul><li>Methodology </li></ul></ul><ul><li>A case study </li></ul><ul><ul><li>Health systems and mortality </li></ul></ul>
    3. 3. Causal analysis <ul><li>Goals </li></ul><ul><ul><li>Detecting causes of effects </li></ul></ul><ul><ul><li>Measuring effects of causes </li></ul></ul><ul><ul><li>Uncovering causal structures </li></ul></ul><ul><ul><li>Modelling causal mechanisms </li></ul></ul><ul><ul><li>… </li></ul></ul><ul><li>Methods </li></ul><ul><ul><li>Qualitative/Quantitative </li></ul></ul><ul><ul><li>Aggregate/Individual/Multilevel </li></ul></ul><ul><ul><li>… </li></ul></ul><ul><li>Methodology </li></ul><ul><ul><li>Hypothetico-deductivism </li></ul></ul>
    4. 4. Causal models are made of <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>Key notions </li></ul><ul><ul><li>Background knowledge </li></ul></ul><ul><ul><li>Exogeneity </li></ul></ul><ul><ul><li>Invariance/Stability </li></ul></ul><ul><ul><li>Closure of the system </li></ul></ul>
    5. 5. Causal assumptions <ul><li>Covariate sufficiency </li></ul><ul><ul><li>All the variables included in the model </li></ul></ul><ul><ul><li>are needed to explain the phenomenon </li></ul></ul><ul><li>No-confounding </li></ul><ul><ul><li>No variable included in the model </li></ul></ul><ul><ul><li>screens-off other variables </li></ul></ul>
    6. 6. Presupposition: the system is closed <ul><li>Strict closure </li></ul><ul><ul><li>The system described is not subject </li></ul></ul><ul><ul><li>to any external influence </li></ul></ul>X Y R V
    7. 7. Presupposition: the system is closed <ul><li>Weak closure </li></ul><ul><ul><li>Variables in the model undergo influences </li></ul></ul><ul><ul><li>from non-observed variables non correlated </li></ul></ul><ul><ul><li>between themselves </li></ul></ul>X Y R V
    8. 8. Presupposition: the system is closed <ul><li>Failure of closure </li></ul><ul><ul><li>Variables in the model undergo influences </li></ul></ul><ul><ul><li>from non-observed variables that are </li></ul></ul><ul><ul><li>correlated between themselves </li></ul></ul>X Y R V
    9. 9. Causal models model mechanisms <ul><li>Mechanisms are a scheme of </li></ul><ul><li>how properties relate to each other </li></ul><ul><li>Variables play specific (causal) roles </li></ul><ul><li>Some types of relations are excluded, </li></ul><ul><li>e.g. loops </li></ul>
    10. 10. Hypothetico-deductivism <ul><li>Hypothesise stage and prior information </li></ul><ul><ul><li>Causal hypotheses are (dis)confirmed </li></ul></ul><ul><ul><li>depending on results of tests and </li></ul></ul><ul><ul><li>on congruence with background knowledge </li></ul></ul><ul><li>A dynamic process </li></ul><ul><ul><li>Va et vient between established theories </li></ul></ul><ul><ul><li>and establishing theories </li></ul></ul>
    11. 11. System analysis: scope and goals <ul><li>System theorists </li></ul><ul><ul><li>Von Bertalanffy (1969), Bunge (1979) </li></ul></ul><ul><li>A general theory of systems </li></ul><ul><li>in the various sciences </li></ul><ul><li>Formulation and derivation of </li></ul><ul><li>principles valid for all systems </li></ul><ul><li>Systems are ubiquitous, </li></ul><ul><li>a general framework is needed </li></ul>
    12. 12. Von Bertalanffy: <ul><li>Major aims of a general system theory (1969): </li></ul><ul><ul><li>1. there is a general tendency towards the integration </li></ul></ul><ul><ul><li>in the various sciences, natural and social; </li></ul></ul><ul><ul><li>2. such integration seems to be centred </li></ul></ul><ul><ul><li>in a general system theory; </li></ul></ul><ul><ul><li>3. such theory may be an important means of aiming </li></ul></ul><ul><ul><li>at exact theory in the non-physical fields of science; </li></ul></ul><ul><ul><li>4. developing unifying principles running ‘vertically’ </li></ul></ul><ul><ul><li>through the universe of the individual sciences; </li></ul></ul><ul><ul><li>5. this can lead to a much-needed integration </li></ul></ul><ul><ul><li>in scientific education. </li></ul></ul><ul><li>General system theory aims to </li></ul><ul><li>encompass various disciplines. </li></ul>
    13. 13. What is a system? <ul><li>A system is a set of elements standing </li></ul><ul><li>in reciprocal interrelations </li></ul><ul><ul><li>Elements, p , stand in relation, R , so that the behaviour of an element p in R is different from its behaviour in another relation, R’ . If the behaviours in R and R’ are not different, there is no interaction, and the elements behave independently with respect to the relations R and R’ . (von Bertalanffy 1969, p.37) </li></ul></ul><ul><li>Systems are mathematically defined </li></ul><ul><li>by certain families of differential equations </li></ul><ul><li>Systems are not aggregates </li></ul><ul><ul><li>(= collections of items not held together </li></ul></ul><ul><ul><li>by bonds and lacking integrity) </li></ul></ul>
    14. 14. Systems and the whole <ul><li>Science of the whole </li></ul><ul><ul><li>Holism: </li></ul></ul><ul><ul><li>stresses integrity of systems at the expenses of </li></ul></ul><ul><ul><li>their components and of mutual actions among them </li></ul></ul><ul><ul><li>Atomism: </li></ul></ul><ul><ul><li>the whole is contained in its parts, so the study </li></ul></ul><ul><ul><li>of parts suffices to understand the whole </li></ul></ul><ul><li>Neither can properly analyse systems </li></ul>
    15. 15. Systemics methodology (Bunge) <ul><li>First </li></ul><ul><ul><li>identification of the components of the system </li></ul></ul><ul><li>Second </li></ul><ul><ul><li>identification of the environment </li></ul></ul><ul><li>Third </li></ul><ul><ul><li>identification of the structure </li></ul></ul><ul><li>N.B.: no prior hypotheses about the structure </li></ul>
    16. 16. Systemics, a different worldview <ul><li>Von Bertalanffy: </li></ul><ul><ul><li>systemics open a new paradigm </li></ul></ul><ul><li>System philosophy </li></ul><ul><ul><li>Against the analytic, mechanistic, </li></ul></ul><ul><ul><li>one-way causal paradigm of classical science </li></ul></ul><ul><ul><li>No sharp difference between </li></ul></ul><ul><ul><li>the object of investigation and the knowing agent </li></ul></ul>
    17. 17. A different worldview <ul><li>von Bertalanffy (1969) </li></ul><ul><ul><li>Perception is not a reflection of ‘real things’ (whatever their metaphysical status), and knowledge is not a simple approximation to ‘truth’ or ‘reality’. It is an interaction between knower and known, this dependent on a multiplicity of factors of a biological, psychological, cultural, linguistic, etc., nature. </li></ul></ul>
    18. 18. A different worldview <ul><li>Von Bertalanffy (1969) </li></ul><ul><ul><li>The third part of systems philosophy will be concerned with the relations of man and world or what is termed ‘ values ’ in philosophical parlance. If reality is a hierarchy of organized wholes, the image of man will be different from what it is in a world of physical particles governed by chance events as ultimate and only ‘true’ reality. Rather, the world of symbols, values, social entities ad cultures is something very ‘real’; and its embeddedness in a cosmic order of hierarchies is apt to bridge the opposition of C.P. Snow’s ‘Two Cultures’ of science and the humanities, technologies and history, natural and social sciences, or in whatever way the antithesis is formulated. </li></ul></ul>
    19. 19. A different worldview <ul><li>Bunge (1979): </li></ul><ul><ul><li>There are no stray things </li></ul></ul><ul><ul><li>Every thing interacts with other things </li></ul></ul><ul><ul><li>so that all things cohere in forming systems </li></ul></ul><ul><ul><li>Every concrete thing is either a system </li></ul></ul><ul><ul><li>or a component of it </li></ul></ul><ul><ul><li>Every system is engaged in some process or other </li></ul></ul><ul><ul><li>Every change in a system is lawful </li></ul></ul>
    20. 20. Causal analysis vs . system analysis <ul><li>Closure of the system </li></ul><ul><li>and mechanisms </li></ul><ul><li>The agent is external </li></ul><ul><li>Causal mechanisms are </li></ul><ul><li>established using </li></ul><ul><li>prior information </li></ul>Every thing interacts with everything else The agent is internal Structures are identified without prior information
    21. 21. Causal analysis within system analysis? <ul><li>Lauriaux (1994) </li></ul><ul><ul><li>theoretical weaknesses of causal analysis: </li></ul></ul><ul><ul><ul><li>choice of variables, conceptualisation, </li></ul></ul></ul><ul><ul><ul><li>closure of the system </li></ul></ul></ul>
    22. 22. A case study: health system and mortality  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
    23. 23. Lauriaux’s critique <ul><li>Principal variables are theoretical constructs </li></ul><ul><li>according to well established economic </li></ul><ul><li>and sociological theories </li></ul><ul><li>Assumption: economic development </li></ul><ul><li>generates social development </li></ul><ul><li>Problem: counterexamples exist, the arrow </li></ul><ul><li>might be reversed with serious problems for policy </li></ul><ul><li>To intervene on an effect which is not an effect </li></ul><ul><li>won’t deliver the planned results </li></ul>
    24. 24. Causal analysis within system analysis? <ul><li>The problem still remains: </li></ul><ul><ul><li>How to make sense of covariations </li></ul></ul><ul><ul><li>between variables if we abandon </li></ul></ul><ul><ul><li>the causal framework? </li></ul></ul><ul><li>Solution: system analysis </li></ul>
    25. 25. Complementarity of the two approaches? <ul><li>Systems are homeostatic: </li></ul><ul><ul><li>they keep themselves in a stable state by means </li></ul></ul><ul><ul><li>of regulatory interdependent mechanisms </li></ul></ul><ul><ul><ul><li>Changes in the system re-establish the </li></ul></ul></ul><ul><ul><ul><li>equilibrium in consequence of too strong </li></ul></ul></ul><ul><ul><ul><li>internal/external influences </li></ul></ul></ul><ul><ul><ul><li>In the process of balancing, </li></ul></ul></ul><ul><ul><ul><li>components jointly evolve </li></ul></ul></ul><ul><ul><ul><li>Those joint evolutions are covariations </li></ul></ul></ul><ul><ul><ul><li>we call causal </li></ul></ul></ul>
    26. 26. Lauriaux’s systemic story
    27. 28. My systemic worries <ul><li>Systems become very easily intractable, </li></ul><ul><li>of difficult use for policy </li></ul><ul><li>I haven’t seen precise, </li></ul><ul><li>concrete methods to analyse data </li></ul><ul><li>Assumptions clash too much to make </li></ul><ul><li>the approaches complementary </li></ul>
    28. 29. To sum up <ul><li>I sketched the features </li></ul><ul><ul><li>Of causal analysis: closure of the system, use of prior information, mechanism </li></ul></ul><ul><ul><li>Of system analysis: different worldview, reciprocal interrelations of elements </li></ul></ul><ul><li>I discussed the possibility of a </li></ul><ul><li>complementarity of the two approaches </li></ul>
    29. 30. To conclude <ul><li>Are those approaches compatible? </li></ul><ul><ul><li>I think not, because of significantly </li></ul></ul><ul><ul><li>different assumptions </li></ul></ul><ul><li>Is systemic a viable alternative? </li></ul><ul><ul><li>I think not, because clear methods </li></ul></ul><ul><ul><li>are still lacking </li></ul></ul>
    30. 31. References <ul><li>Bunge M. (1979), A world of systems. </li></ul><ul><li>Franck R. (1994) (ed), Faut-il chercher aux causes une raison? </li></ul><ul><li>Franck R. (2002) (ed), The Explanatory Power of Models . </li></ul><ul><li>Lauriaux M. (1994),  “ Des causes aux syst èmes: la causalité en question”, in Franck (1994). </li></ul><ul><li>Lopez-Rios O., Mompart A. and Wunsch G. (1992), “Système de soins et mortalité régionale: une analyse causale”, European Journal of Population , 8(4), 363-379. </li></ul><ul><li>Pumain D. (2006), Hierarchy in natural and social sciences . </li></ul><ul><li>Russo F. (forthcoming), Measuring variations. Causality and causal modelling in the social sciences. </li></ul><ul><li>von Bertalanffy (1969), General system theory. </li></ul>

    ×