Abstract
A popular approach to the framing and answering of causal questions relies on the idea of counterfactuals: outcomes that would have been observed had the world developed differently, e.g. if the patient had received a different treatment. By definition, we can never observe such quantities, nor can we assess empirically the validity of any modelling assumptions we may make about them, even though our conclusions may be sensitive to these assumptions. Here we argue that, for making inference about the likely effects of applied causes, counterfactual arguments are unnecessary and potentially misleading. An alternative approach, based on Bayesian decision analysis, is presented. Properties of counterfactuals are relevant to inference about the likely causes of observed effects, but then close attention to what can and cannot be supported empirically is needed to qualify the conclusions drawn, and unambiguous inferences will generally only be possible when they can be based on an essentially deterministic theory.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cox, D. R. (1958): The interpretation of the effects of non-additivity in the Latin Square. Biometrika, 45, 69–73
Dawid, A. P. (1985): Calibration-based empirical probability (with Discussion). Ann. Statist. 13, 1251–1285
Dawid, A. P. (1988): Symmetry models and hypotheses for structured data layouts (with Discussion). J. Roy. Statist. Soc. B, 50, 1–34
Heckerman, D. and Shachter, R. (1995): Decision-theoretic foundations for causal reasoning. J. Artificial Intell. Res. 3, 40–430
Holland, P. W. (1986): Statistics and causal inference (with Discussion). J. Am. Statist. Assoc. 81, 945–970
Imbens, G. W. and Rubin, D. B. (1997): Bayesian inference for causal effects in randomized experiments with noncompliance. Ann. Statist. 25, 305–327
McCullagh, P. and Nelder, J. A. (1989): Generalized Linear Models. Second Edition. London: Chapman & Hall
Neyman, J. (1923): On the application of probability theory to agricultural experiments. Essay on principles. Roczniki Nauk Rolniczych X, 1–51 (in Polish). English translation of Section 9 (D. M. Dabrowska and T. P. Speed): Statistical Science, 9 (1990), 465-480
Neyman, J. (1935): Statistical problems in agricultural experimentation (with Discussion). Suppl. J. Roy. Statist. Soc. 2, 107–180
Pascal, B. (1669): Pensées sur la Réligion, et sur Quelques Autres Sujets. Paris: Guillaume Desprez. (Edition Gamier Frères, 1964.)
Pearl, J. (1995): Causal diagrams for empirical research (with Discussion). Biometrika 82, 669–710
Raiffa, H. (1968): Decision Analysis: Introductory Lectures on Choices under Uncertainty. Addison-Wesley, Reading, MA
Robins, J. (1986): A new approach to causal inference in mortality studies with sustained exposure periods-application to control of the healthy worker survivor effect. Math. Modelling, 7, 1393–1512
Robins, J. (1987): Addendum to “A new approach to causal inference in mortality studies with sustained exposure periods-application to control of the healthy worker survivor effect. ” Comp. Math. Appl. 14, 923–945
Rubin, D. B. (1974): Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66, 688–701
Rubin, D. B. (1978): Bayesian inference for causal effects: the role of randomization.Ann. Statist. 6, 34–68
Rubin, D. B. (1980): Comment on “Randomization analysis of experimental data: the Fisher randomization test”, by D. Basu. J. Am. Statist. Assoc. 81, 961–962
Rubin, D. B. (1986): Which Ifs have causal answers? (Comment on “Statistics and causal inference”, by P. W. Holland). J. Am. Statist. Assoc. 81, 961–962
Shafer, Glenn (1996): The Art of Causal Conjecture. MIT Press, Cambridge, MA
Wilk, M. B. and Kempthorne, O. (1955): Fixed, mixed and random models. J. Am. Statist. Ass. 50, 1144–1167
Wilk, M. B. and Kempthorne, O. (1956): Some aspects of the analysis of factorial experiments in a completely randomized design. Ann. Math. Statist. 27, 950–985
Wilk, M. B. and Kempthorne, O. (1957): Non-additivities in a Latin square. J. Am. Statist. Assoc. 52, 218–236
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Dawid, A.P. (1999). Who Needs Counterfactuals?. In: Gammerman, A. (eds) Causal Models and Intelligent Data Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-58648-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-58648-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-63682-0
Online ISBN: 978-3-642-58648-4
eBook Packages: Springer Book Archive