L’inférence causale par contrefactuels dans les études observationnelles — Quelques épistémologiques : Cet article contribue au débat sur les vertus et les vices de contrefactuels comme base pour l’inférence causale. L’objectif est de mettre l’approche contrefactuelle dans une perspective épistémologique. Nous discutons d’un certain nombre de questions, allant de sa base non observable au parallélisme établi entre cette approche en statistique et en philosophie. Nous soutenons que la question n’est pas de rejeter ou d’approuver l’approche contrefactuelle par principe, mais de décider quel cadre de modélisation est préférable en fonction du contexte de la recherche.
This paper contributes to the debate on the virtues and vices of counterfactuals as a basis for causal inference. The goal is to put the counterfactual approach in an epistemological perspective. We discuss a number of issues, ranging from its non-observable basis to the parallelisms drawn between the counterfactual approach in statistics and in philosophy. We argue that the question is not to oppose or to endorse the counterfactual approach as a matter of principle, but to decide what modelling framework is best to adopt depending on the research context.
Amanzio M., Pollo A., Maggi G., Benedetti F. ( 2001) Response Variability to Analgesics: A Role for Non-specific Activation of Endogenous Opioids. Pain. 90(3): 205-215.
2.
American Cancer Society (2010) Breast Cancer Overview. Available at: http://www.cancer.org/
3.
Bouckaert A., Mouchart M. ( 2001) Sure Outcomes of Random Events: A Model for Clinical Trials . Statistics in Medicine20: 521-543.
4.
Brand JE, Xie Y. ( 2007) Identification and Estimation of Causal Effects with Time-varying Treatments and Time-varying Outcomes. Sociological Methodology37(1): 393-434.
5.
Bryson A., Dorsett R., Purdon S. ( 2002) The Use of Propensity Score Matching in the Evaluation of Active Labour Market Policies. Working Paper Number4, Department Work and Pensions, London.
6.
Collins J, Hall N, Paul LA (eds) (2004) Causation and Counterfactuals. MIT Press: Cambridge, MA.
7.
Dawid AP ( 2000) Causal Inference without Counterfactuals. Journal of the American Statistical Association95(450): 407-424.
8.
Dawid AP ( 2007) Counterfactuals, Hypotheticals and Potential Responses: A Philosophical Examination of Statistical Causality. In: Russo F, Williamson J (eds) Causality and Probability in the Sciences. College Publications: London, 503-532.
9.
Dehejia RH, Wahba S. ( 2002) Propensity Score-matching Methods for Nonexperimental Causal Studies. Review of Economics and Statistics84(1): 151-161.
10.
Ducheyne S. ( 2005) Joan Baptiste van Helmont and the Question of Experimental Modernism. Physis: Rivista Internazionale di Storia della Scienza43: 305-332.
11.
Fisher RA ( 1925) Statistical Methods for Research Workers. Oliver and Boyd: Edinburgh.
12.
Fisher RA ( 1935) The Design of Experiments. Oliver and Boyd: Edinburgh.
13.
Frangakis CE, Rubin DB ( 2002) Principal Stratification in Causal Inference. Biometrics58: 21-29.
14.
Gaumé C., Wunsch G (2010) Self-rated Health in the Baltic Countries, 1994-1999. European Journal of Population26(4): 435-457.
15.
Glasziou P., Chalmers I., Rawlins M., McCulloch P. ( 2007) When Are Randomised Trials Unnecessary? Picking Signal from Noise. British Medical Journal334: 349-351.
16.
Hacking I. ( 1988) Telepathy: Origins of Randomization in Experimental Design . Isis79(3): 427- 451.
17.
Hall NS ( 2007) R. A. Fisher and His Advocacy of Randomization. Journal of History of Biology40: 295-325.
18.
Heckman J. ( 2005) The Scientific Model of Causality. Sociological Methodology35(1): 1-97.
19.
Heckman J. ( 2008) Econometric Causality. International Statistical Review76(1): 1-27.
20.
Hedström P., Swedberg R. ( 1998) Social Mechanisms. Cambridge University Press:Cambridge.
21.
Holland PW ( 1986) Statistics and Causal Inference. Journal of the American Statistical Association81(396): 945-960.
22.
Holland PW ( 1988) Comment: Causal Mechanism or Causal Effect: Which Is Best for Statistical Science? Statistical Science3(2): 186-188.
23.
Holland PW ( 2001) The False Linking of Race and Causality: Lessons from Standardized Testing. Race & Society4: 219-233.
24.
Hood WC, Koopmans TC (eds) (1953) Studies in Econometric Method . Wiley: New York.
25.
Hume D. ( 1748) An Enquiry Concerning Human Understanding. Bobbs-Merrill: Indianapolis (1955 ).
26.
Imbens GW, Rubin DR ( 1995) Discussion of ‘‘Causal Diagrams for Empirical Research’’ byJ. Pearl. Biometrika82(4): 694-695.
27.
Jones B. ( 2008) The Cross-over Trial: A Subtle Knife. Significance5(3): 135-137.
28.
Kaufman JS, Cooper RS ( 1999) Seeking Causal Explanations in Social Epidemiology. American Journal of Epidemiology150(2): 113-120.
29.
Klein J. ( 2008) Francis Bacon’s Scientia Operativa, The Tradition of the Workshops, and the Secrets of Nature. In: Zittel C, Nanni R, Engel G, Karafyllis N (eds), Philosophies of Technology: Francis Bacon and his Contemporaries. Brill E-Books. DOI:101163/ej. 9789004170506.i-582.1.
30.
Klein J. ( 2009) Francis Bacon. Stanford Encyclopaedia of Philosophy. Available at : http://plato.stanford.edu/archives/spr2009/entries/francis-bacon/
31.
Lewis D. ( 1973a) Causation. Journal of Philosophy70: 556-567. Reprinted with postscripts in Lewis D. (1986) Philosophical Papers II. Oxford University Press: Oxford, 159-213.
32.
Lewis D. ( 1973b), Counterfactuals. Harvard University Press: Cambridge.
33.
Lewis D. ( 1979) Counterfactual Dependence and Time’s Arrow. Noûs13(4): 455-476.
34.
Lewis D. ( 2004) Causation as Influence. In: Collins J, Hall N, Paul LA (eds) (2004) Causation and Counterfactuals. MIT Press: Cambridge, MA.
35.
Mealli F., Rubin DB ( 2002) Assumptions When Analyzing Randomized Experiments with Noncompliance and Missing Outcomes. Health Services & Outcomes Research Methodology3: 225-232.
36.
Menzies P. ( 2009) Counterfactual Theories of Causation. Stanford Encyclopedia of Philosophy. Available at: http://plato.stanford.edu/archives/fall2009/entries/causation-counterfactual/
37.
Morgan SL, Harding DJ ( 2006) Matching Estimators of Causal Effects. Prospects and Pitfalls in Theory and Practice. Sociological Methods & Research35(1): 3-60.
38.
Morgan SL, Winship C. ( 2007) Counterfactuals and Causal Inference. Cambridge University Press:New York.
39.
Mouchart M., Russo F., Wunsch G. ( 2009) Structural Modelling, Exogeneity, and Causality. In: Engelhardt H, Kohler HP, Fürnkranz-Prsawetz A (eds) Causal Analysis in Population Studies. Concepts, Methods, Applications. Dordrecht: Springer, 59-82.
40.
Mouchart M., Russo F. ( 2011) Causal Explanation. Mechanisms and Recursive Decomposition . In: McKay Illari P, Russo F, Williamson J (eds) Causality in the Sciences. Oxford University Press: Oxford, pp. 317-337.
41.
Muntaner C. ( 1999) Invited Commentary: Social Mechanisms, Race, and Social Epidemiology. American Journal of Epidemiology150(2): 121-126.
42.
Ni Bhrolchain M., Dyson T. ( 2007) On Causation in Demography: Issues and Illustrations. Population and Development Review33(1): 1-36.
43.
Pearl J. ( 1995) Rejoinder to Discussions of ‘‘Causal Diagrams for Empirical Research’’. Biometrika82(4): 702-710.
44.
Pearl J. ( 2000) Causality. Models, Reasoning, and Inference. Cambridge University Press:Cambridge.
45.
Pearl J. ( 2009) Letter to the Editor: Remarks on the Method of Propensity Score. Statistics in Medicine28: 1415-1416.
46.
Peirce CS, Jastrow J. ( 1885) On Small Differences in Sensation. Memoirs of the National Academy of Sciences3:73-83. Accessible at: http://psychclassics.yorku.ca/Peirce/small-diffs.htm
47.
Rescher N. ( 1978) Pierce’s Philosophy of Science. University of Notre Dame Press: Notre Dame.
48.
Rosenbaum PR, Rubin DB ( 1983) The Central Role of the Propensity Score in Observational Studies for Causal Effect. Biometrika70(1): 41-55.
49.
Rubin DB ( 1974) Estimating Causal Effects of Treatments in Randomized and Non Randomized Studies. Journal of Educational Psychology66(5): 688-701.
50.
Rubin DB ( 1986) Which Ifs Have Causal Answers. Journal of the American Statistical Association81(396): 961-962.
51.
Rubin DB ( 1990) Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies. Statistical Science5(4): 472-480.
52.
Rubin DB ( 2001) Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation. Health Services & Outcomes Research Methodology2: 169-188.
53.
Rubin DB ( 2004) Direct and Indirect Causal Effects via Potential Outcomes . Scandinavian Journal of Statistics31: 161-170.
54.
Rubin DB ( 2007) The Design versus the Analysis of Observational Studies for Causal Effects: Parallels with the Design of Randomized Trials. Statistics in Medicine26(1): 20-36.
55.
Russo F. ( 2009a) Variational Causal Claims in Epidemiology. Perspectives in Biology and Medicine52(4): 540-554.
56.
Russo F. ( 2009b) Causality and Causal Modelling in the Social Sciences . Measuring Variations. Springer: New York.
57.
Russo F. ( 2011) Causal Webs in Epidemiology. Paradigmi, Special Issue on the Philosophy of Medicine: in press.
58.
Russo F. (forthcoming) Correlational Data, Causal Hypotheses, and Validity. Journal of General Philosophy of Science.
59.
Russo F., Williamson J. ( 2007) Interpreting Causality in the Health Sciences. International Studies in Philosophy of Science21(2): 157-170.
60.
Russo F., Williamson J. ( 2011) Generic vs. Single-case Causal Knowledge. The Case of Autopsy. European Journal for Philosophy of Science, Online first, 4 January 2011, DOI: 10.1007/ s13194-010-0012-4.
61.
Sobel ME ( 1995) Causal Inference in the Social and Behavioural Sciences . In: Arminger G, Clogg CC, Sobel ME (eds) Handbook of Statistical Modeling for the Social and Behavioral Sciences. NewYork: Plenum, 1-38.
62.
Sobel ME ( 2000) Causal Inference in the Social Sciences. Journal of the American Statistical Association95(450): 647-651.
Williamson J. ( 2005) Bayesian Nets and Causality. Philosophical and Computational Foundations. Oxford University Press: Oxford.
65.
Williamson J. ( 2006a) Causal Pluralism versus Epistemic Causality. Philosophica77: 69-96.
66.
Williamson J. ( 2006b) Dispositional versus Epistemic Causality. Minds and Machines16: 259-276.
67.
Winship C., Morgan SL ( 1999) The Estimation of Causal Effects from Observational Data . Annual Review of Sociology25: 659-707.
68.
Woodward J. ( 2003) Making Things Happen. A Theory of Causal Explanation. Oxford University Press: New York.
69.
Wunsch G. ( 2007) Confounding and Control. Demographic Research . 16: 15-35.
70.
Wunsch G., Russo F., Mouchart M. ( 2010) Do We Necessarily Need Longitudinal Data to Infer Causal Relations?Bulletin de Méthodologie Sociologique106: 1-14.
