Abstract
This contribution addresses a number of issues related to the representation, use and appraisal of evidence, with a special focus on the health sciences and law. It is argued that evidence is a trans-disciplinary notion whose distinctive trait is its capacity to provide a link between some body of information and some hypothesis such information supports or negates. As such, evidence is strictly associated with relevance, and like relevance it is intrinsically context-dependent. An analysis of evidence has to address a number of issues, including the epistemic context of reference, the general or particular nature of the hypothesis under scrutiny, the predictive or explanatory character of the inference in which evidence is involved, and the stage at which a given body of evidence is being used within a complex inferential process. Moreover, an awareness of the context in which evidence is appraised recommends that all assumptions underlying the representation of evidence be rigorously spelled out and justified case by case, and the ultimate aims of evidence be clearly specified.
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Notes
- 1.
See also the collection of papers in Suppes (1993).
- 2.
Questions of this kind have been the focus of the interdisciplinary research supported by Leverhulme Foundation “Evidence, inference and enquiry: Towards an integrated science of evidence,” carried out between 2004 and 2007 under the guidance of the statistician Philip Dawid. This research project led to the publication of Dawid et al. eds. (2011b).
- 3.
See for instance a recent issue of the journal Law, Probability, and Risk, 9 (2010), n. 3–4, entirely devoted to “Risk and probability in bioethics.”
- 4.
- 5.
- 6.
These and other related issues are addressed in Redmayne (2001).
- 7.
The literature on statistics in law reflects an increasing awareness of the importance of this problem. See for instance Taggart and Blackmon 2008.
- 8.
Some of the objections to the use of probability and statistics in court are discussed in Galavotti (2012). For a discussion of Bayesian methods in the law see Fienberg and Finkelstein (1996). An interesting comparison between the Bayesian and frequentist approaches to a DNA identification problem is to be found in Kaye (2008).
- 9.
For an extensive discussion of the prosecutor’s fallacy see Gigerenzer (2002).
- 10.
- 11.
For an extensive treatment of Bayesian networks and their use in forensic science see Taroni et al. (2006).
- 12.
This is emphasized in Dawid et al. (2011a), which contains a detailed comparison of Bayesian and Wigmorean networks.
References
Aitken, C. G. G. (1995). Statistics and the evaluation of evidence for forensic scientists. Chichester: Wiley.
Anderson, T., & Twining, W. (1991). Analysis of evidence. London: Weidenfeld and Nicholson.
Campaner, R. (2011). Understanding mechanisms in the health sciences. Theoretical Medicine and Bioethics, 32, 5–17.
Campaner, R., & Galavotti, M. C. (2007). Plurality in causality. In P. Machamer & G. Wolters (Eds.), Thinking about causes (pp. 178–199). Pittsburgh: University of Pittsburgh Press.
Campaner, R., & Galavotti, M. C. (2012). Evidence and the assessment of the causal relations in the health sciences. European Studies in the Philosophy of Science, 26, 27–45.
Dawid, P. (2000). Causal inference without counterfactuals. Journal of the American Statistical Association, 95, 407–424.
Dawid, P. (2002). Bayes’s theorem and weighing evidence by juries. In R. Swinburne (Ed.), Bayes’s theorem, proceedings of the British Academy 113 (pp. 71–90). Oxford: Oxford University Press.
Dawid, P. (2005a). Probability and statistics in court. Appendix online to the second edition of T. Anderson, D. Schum, & W. Twining (Eds.), Analysis of evidence. Cambridge: Cambridge University Press. http://tinyurl.com/7q3bd. Accessed 14 Jan 2013.
Dawid, P. (2005b). Probability and statistics in the law. In R. G. Cowell & Z. Ghahramani (Eds.), Proceedings of the tenth international workshop on artificial intelligence and statistics (AISTATS 2005). http://tinyurl.com/br8fl . Accessed 14 Jan 2013.
Dawid, P. (2007). Counterfactuals, hypotheticals and potential responses: A philosophical examination of statistical causality. In F. Russo & J. Williamson (Eds.), Causality and probability in the sciences (pp. 503–532). London: College Publications.
Dawid, P. (2008). Statistics and the law. In A. Bell, J. Swenson-Wright, & K. Tybjerg (Eds.), Evidence (pp. 119–148). Cambridge: Cambridge University Press.
Dawid, P., Schum, D., & Hepler, A. (2011a). Inference networks: Bayes and Wigmore. In P. Dawid, W. Twining, & M. Vasilaki (Eds.), Evidence, inference and enquiry. Proceedings of the British Academy 171 (pp. 119–150). Oxford: Oxford University Press.
Dawid, P., Twining, W., & Vasilaki, M. (Eds.). (2011b). Evidence, inference and enquiry. Proceedings of the British Academy 171. Oxford: Oxford University Press.
Evett, I. W. (1991). Interpretation: A personal odyssey. In C. G. G. Aitken & D. A. Stoney (Eds.), The use of statistics in forensic science (pp. 9–22). New York: Ellis Horwood.
Fienberg, S., & Finkelstein, M. O. (1996). Bayesian statistics and the law. In J. M. Bernardo, J. O. Berger, P. Dawid, & A. F. M. Smith (Eds.), Bayesian statistics (pp. 129–146). Oxford: Oxford University Press.
Galavotti, M. C. (2001). Causality, mechanisms and manipulation. In M. C. Galavotti, P. Suppes, & D. Costantini (Eds.), Stochastic causality (pp. 1–14). Stanford: CSLI.
Galavotti, M. C. (2008). Causal pluralism and context. In M. C. Galavotti, R. Scazzieri, & P. Suppes (Eds.), Reasoning, rationality and probability (pp. 233–252). Stanford: CSLI.
Galavotti, M. C. (2010). Probabilistic causality, observation and experimentation. In W. J. Gonzalez (Ed.), New methodological perspectives on observation and experimentation in science (pp. 139–155). A Coruña: Netbiblo.
Galavotti, M. C. (2012). Probability, statistics, and law. In D. Dieks, W. J. Gonzalez, S. Hartmann, M. Stoeltzner, & M. Weber (Eds.), Probability, laws, and structures (pp. 401–412). Dordrecht: Springer.
Gigerenzer, G. (2002). Reckoning with risk: Learning to live with uncertainty. New York: Simon and Schuster.
Gillies, D. (2011). The Russo-Williamson thesis and the question of whether smoking causes heart disease. In P. M. Illari, F. Russo, & J. Williamson (Eds.), Causality in science (pp. 110–125). Oxford: Oxford University Press.
Glennan, S. (2002). Rethinking mechanical explanation. Philosophy of Science, 69, S342–S353.
Glennan, S. (2010). Mechanisms, causes and the layered model of the world. Philosophy and Phenomenological Research, 81, 362–381.
Good, I. J. (1961–1962). A causal calculus. Part I and II. British Journal for the Philosophy of Science, 11, 305–318; 12, 43–51; Errata and Corrigenda 13, 88.
Howick, J., Glasziou, P., & Aronson, J. (2009). The evolution of evidence hierarchies: What can Bradford Hill’s ‘guidelines for Causation’ contribute? Journal of the Royal Society of Medicine, 102, 186–194.
Kaye, D. (2008). Case comment – People v. Nelson: A tale of two statistics. Law, Probability and Risk, 7, 249–257.
Kringelbach, M. L., Jenkinson, N., Owen, S., & Tipu, A. (2007). Translational principles of deep brain stimulation. Nature Review Neuroscience, 8, 623–635.
Lagnado, D. (2011). Thinking about evidence. In P. Dawid, W. Twining, & M. Vasilaki (Eds.), Evidence, inference and enquiry. Proceedings of the British Academy 171 (pp. 183–224). Oxford: Oxford University Press.
Lindley, D. V. (1975). Probabilities and the law. In D. Wendt & C. Vlek (Eds.), Utility, probability, and human decision making (pp. 223–232). Dordrecht/Boston: Reidel.
Lindley, D. V. (1991). Probability. In C. G. G. Aitken & D. A. Stoney (Eds.), The use of statistics in forensic science (pp. 27–50). New York: Ellis Horwood.
Machamer, P., Darden, L., & Craver, C. (2000). Thinking about mechanisms. Philosophy of Science, 67, 1–25.
Price, H. (1991). Agency and probabilistic causality. British Journal for the Philosophy of Science, 42, 157–176.
Price, H. (2007). Causal perspectivalism. In H. Price & R. Corry (Eds.), Causation, physics, and the constitution of reality. Russell’s republicanism revisited (pp. 250–292). Oxford: Clarendon Press.
Redmayne, M. (2001). Expert evidence and criminal justice. Oxford: Oxford University Press.
Reichenbach, H. (1938). Experience and prediction. Chicago/London: Chicago University Press. 6th edition 1966.
Rid, A., & Wendler, D. (2010). Risk-benefit assessment in medical research – Critical review and open questions. Law Probability and Risk, 9, 151–177.
Robertson, B., & Vignaux, R. (1995). Interpreting evidence. Chichester: Wiley.
Russo, F., & Williamson, J. (2007). Interpreting causality in the health sciences. International Studies in the Philosophy of Science, 21, 157–170.
Russo, F., & Williamson, J. (2011). Generic versus single-case causality: The case of autopsy. European Journal for the Philosophy of Science, 1, 47–69.
Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton: Princeton University Press.
Salmon, W. C. (1998). Causality and explanation. New York/Oxford: Oxford University Press.
Salmon, W. C. (2002). A realistic account of causation. In M. Marsonet (Ed.), The problem of realism (pp. 106–134). Aldershot: Ashgate.
Savage, L. J. (1977). The shifting foundations of statistics. In R. Colodny (Ed.), Logic, laws, and life (pp. 3–18). Pittsburgh: University of Pittsburgh Press.
Schum, D. A. (1993). Argument structuring and evidence evaluation. In R. Hastie (Ed.), Inside the juror (pp. 175–191). Cambridge: Cambridge University Press.
Schum, D. A. (2011). Classifying forms and combinations of evidence: Necessary in a science of evidence. In P. Dawid, W. Twining, & M. Vasilaki (Eds.), Evidence, inference and enquiry. Proceedings of the British Academy 171 (pp. 11–36). Oxford: Oxford University Press.
Suppes, P. (1962). Models of data. In E. Nagel, P. Suppes, & A. Tarski (Eds.), Logic, methodology and philosophy of science (pp. 252–261). Stanford: Stanford University Press.
Suppes, P. (1988). Empirical structures. In E. Scheibe (Ed.), The role of experience in science (pp. 23–33). Berlin/New York: Walter de Gruyter.
Suppes, P. (1993). Models and methods in the philosophy of science: Selected essays. Dordrecht/Boston: Kluwer.
Suppes, P. (2002). Representation and invariance of scientific structures. Stanford: CSLI.
Taggart, A., & Blackmon, W. (2008). Statistical base and background rates: The silent issue not addressed in Massachusetts v. EPA. Law, Probability and Risk, 7, 275–304.
Taroni, F., Aitken, C., Garbolino, P., & Biedermann, A. (2006). Bayesian networks and probabilistic inference in forensic science. Chichester: Wiley.
Thagard, P. (1998). Ulcers and bacteria I: Discovery and acceptance. Studies in History and Philosophy of Science. Part C: Studies in History and Philosophy of Biology and Biomedical Sciences, 29, 107–136.
Twining, W. (2003). Evidence as a multi-disciplinary subject. Law, Probability and Risk, 2, 91–107.
Twining, W. (2011). Moving beyond law: Interdisciplinarity and the study of evidence. In P. Dawid, W. Twining, & M. Vasilaki (Eds.), Evidence, inference and enquiry. Proceedings of the British Academy 171 (pp. 73–118). Oxford: Oxford University Press.
Vineis, P., & Ghisleni, M. (2004). Risks, causality and the precautionary principle. Topoi, 23, 203–210.
Wigmore, J. H. (1913). The science of judicial proof as given by logic, psychology, and general experience, and illustrated in judicial trials. Boston: Little Brown, and Co., 3rd ed. 1937.
Woodward, J. (2003). Making things happen: A theory of causal explanation. New York: Oxford University Press.
Woodward, J. (2004). Counterfactuals and causal explanation. International Studies in the History and Philosophy of Science, 18, 41–72.
Worrall, J. (2006). Why randomize? Evidence and ethics in clinical trials. In W. J. Gonzalez & J. Alcolea (Eds.), Contemporary perspectives in philosophy and methodology of science (pp. 65–82). A Coruña: Netbiblo.
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Galavotti, M.C. (2014). On Representing Evidence. In: Gonzalez, W.J. (eds) Bas van Fraassen’s Approach to Representation and Models in Science. Synthese Library, vol 368. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7838-2_5
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