Virtues and vices of causal modelling. A primer for the next generation of scientists
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Virtues and vices of causal modelling. A primer for the next generation of scientists
- 1. Virtues and vices of causal modelling A primer for the next generation of scientists Federica Russo Philosophy | Humanities | Amsterdam russofederica.wordpress.com | @federicarusso
- 2. Overview Psychological phenomena What is it that we want to model? Modelling psychological phenomena What is a causal model? How does it work? Caveats, pros & cons What’s good (or not so good) about causal models? 2
- 4. 4
- 5. MODELLING PSYCHOLOGICAL PHENOMENA The quantitative approach 5
- 6. Causal models Structural equation models, covariance structure models, contingency tables, multilevel models, regression models, Bayesian networks, potential outcome models, quasi- experimental models, spatial models … Quantitative models: statistical models 6
- 7. Causal modelling has a long tradition Staunch causalists Quetelet, Durkheim, Wright …, Blalock, Duncan, … Make social & behavioural sciences objective Moderate skeptics Pearl, Heckman, Hoover, … Quantitative models do not ipso facto guarantee causality … and the evergreen question: When / how / under what conditions can we infer causation from correlation? 7
- 8. Data and variables What is data? Observations of the characteristics of the studied individuals and populations socio-demo-economic, biological, behavioural, … ‘Organise’ data: variables Genre and scale: continuous / discrete; quantitative / qualitative Role: obervational, latent, instrumental, proxy Level: individual, aggregate Field: socio-economic, demographic, bological, … 8
- 9. Variables and equations Order variables in (systems of) equations How a variable change depending on other variables Establish whether such change is an effect of the change in other variables Vexata quaestio: Causal inference, probability and causality 9
- 10. Quantitative methods Models that establish associations Models that establish causal relations Information having mere statistical import Information having causal import 10
- 11. Associational Models Causal Models Background Knowledge Choice of variables Causal context; Theoretical knowledge; Institutional knowledge; … Assumptions Statistical Statistical; Extra-statistical; Causal Methodology Model-based statistical induction Hypothetico- deduction 11
- 12. H-D methodology 1. Formulate the causal hypothesis 2. Build the statistical model 3. Test the model 4. Check congruence of results with background knowledge Identify the research question; conceptual variables; indicators; … Conditional independence properties; invariance; exogeneity; … Do results make sense? Do they feed further research? … Specify which assumption are statistical, extra- statistical, or causal 12
- 13. Information contained in quantitative models Statistical A summary of data Inferential statistics (sample to population) Adequate and parsimonious description of the phenomenon Statistical dependence Causal Opening the ‘black box’ From association to causation Finer grained analysis of statistical dependence; recursive decomposition Background ‘constraints’ Temporal priority, known causal priority, … Tests Exogeneity, invariance and stability, … 13
- 14. In sum To establish causal relations We need background knowledge And to go beyond it We need causal information And to test for it 14
- 15. CAVEATS, PROS & CONS 15
- 16. Subtleties How much background knowledge? Just the right amount … What kind of causal information? Just the relevant one … A vicious circle introduced? Not quite … 16
- 17. Questions about measurement Age • Very easy to measure • What does it represent? • Does it have any explanatory import? Socio-economic status • Very controversial how we should measure it • What does it represent? • What is its import in explanation of social or social / health outcomes? 17
- 18. Where do we get information from?
- 19. Quantitative studies Large samples, large data sets Correlations to be validated The bigger the better, the more precise the better – true? 19
- 20. Qualitative studies Small samples, small numbers Detailed description of practices Does ‘small’ allow for generalisation? 20
- 21. Experiments Small samples, small numbers Representative sample? Controlled conditions? 21
- 22. TO SUM UP AND CONCLUDE 22
- 23. Against methodological imperialism No method is intrinsically better than others Rigour is not an intrinsic property of methods Validity is not a property of the method, but of the whole process of model building and testing 23
- 24. For methodological pluralism Understanding (psychological) phenomena Hardly ‘objective’ facts Approach them from several methodological angles Figure out What method is best for the phenomenon at hand How else to gather useful information 24
- 25. Don’t be afraid to say ‘cause’ Be rigorous when you say it Always justify: data, method, background knowledge, interpretation of results 25