European Journal for Philosophy of Science

, Volume 8, Issue 1, pp 3–49 | Cite as

Epistemology of causal inference in pharmacology

Towards a framework for the assessment of harms
  • Jürgen Landes
  • Barbara OsimaniEmail author
  • Roland Poellinger
Original Paper in Philosophy of Science


Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s (1965) viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance (external validity), and reliability.


Causation Evidence Bayesian epistemology Scientific inference Safety assessment in pharmacology Risk Bradford Hill criteria 



This paper was presented at various workshops and conferences in Munich, New Brunswick, Sheffield, Helsinki, Durham, Amsterdam, and Ferrara. We greatly profited from the comments and suggestions made by the audiences; in particular we wish to thank Rani Lill Anjum, Timo Bolt, Giovanni Boniolo, Branden Fitelson, Bennett Holman, Phyllis Illari, Mike Kelly, Ulrich Mansmann, Carlo Martini, Julian Reiss, Stephen Senn, Beth Shaw, Jacob Stegenga, and Veronica Vieland. We also thank the focus group members of the ERC project “Philosophy of Pharmacology: Safety, Statistical Standards, and Evidence Amalgamation”, to whom we owe a considerable improvement of the paper’s argumentation: Jeffrey Aronson, Lorenzo Casini, Brendan Clarke, Vincenzo Crupi, Sebastian Lutz, Federica Russo, Glenn Shafer, Jan Sprenger, David Teira, and Jon Williamson. We are extremely grateful to our colleagues at the Munich Center for Mathematical Philosophy, who helped us clarify the objectives and scope of our research, and suggested possible paths of development; in particular we wish to thank Seamus Bradley, Richard Dawid, Samuel C. Fletcher, Stephan Hartmann, Alexander Reutlinger, and Gregory Wheeler. Finally, we thank two anonymous reviewers for their comments. These significantly helped us refine some important assumptions in our theoretical framework. Of course any inaccuracies or errors in the text are, however, our own responsibility.


  1. Abernethy, D., & Bai, G. (2013). Systems pharmacology to predict drug toxicity: integration across levels of biological organization. Annual Review of Pharmacology and Toxicology, 53, 451–73. doi: 10.1146/annurev-pharmtox-011112-140248.CrossRefGoogle Scholar
  2. Allmers, H., Skudlik, C., & John, S. M. (2009). Acetaminophen use: a risk for asthma? Current Allergy and Asthma Reports, 9(2), 164–7. doi: 10.1007/s11882-009-0024-3.CrossRefGoogle Scholar
  3. Anjum, R. L., & Mumford, S. (2012). Causal dispositionalism. Properties, Powers and Structure 101–118, 7 In Bird, A., Ellis, B., & Sankey, H. (Eds.), Routledge.Google Scholar
  4. Baetu, T. M. (2016). The ‘Big picture’: the problem of extrapolation in basic research. British Journal for the Philosophy of Science, 67(4), 941–964. doi: 10.1093/bjps/axv018.CrossRefGoogle Scholar
  5. Bartha, P. (2013). Analogy and analogical reasoning. In Zalta, E.N. (Ed.), The Stanford encyclopedia of philosophy, fall 2013 edn.Google Scholar
  6. Bartha, P. F. A. (2010). By parallel reasoning: the construction and evaluation of analogical arguments. Oxford University Press.Google Scholar
  7. Bes-Rastrollo, M., Schulze, M. B., Ruiz-Canela, M., & Martinez-Gonzalez, M. A. (2013). Financial conflicts of interest and reporting bias regarding the association between sugar-sweetened beverages and weight gain: a systematic review of systematic reviews. PLOS Medicine, 10(12), 1–9. doi: 10.1371/journal.pmed.1001578.CrossRefGoogle Scholar
  8. BonJour, L. (2010). Epistemology. Classic problems and contemporary responses. Rowman & Littlefield Publishers.Google Scholar
  9. Bovens, L., & Hartmann, S. (2003). Bayesian epistemology. Oxford University Press.Google Scholar
  10. Britton, O. J., Bueno-Orovio, A., Van Ammel, K., Lu, H. R., Towart, R., Gallacher, D. J., & Rodriguez, B. (2013). Experimentally calibrated population of models predicts and explains intersubject variability in cardiac cellular electrophysiology. Proceedings of the National Academy of Sciences, 110(23), E2098–E2105. doi: 10.1073/pnas.1304382110.
  11. Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafo, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14, 365–376. doi: 10.1038/nrn3475.CrossRefGoogle Scholar
  12. Carnap, R. (1947). On the application of inductive logic. Philosophy and Phenomenological Research, 8(1), 133–148. Scholar
  13. Carné, X., & Cruz, N. (2005). Ten lessons to be learned from the withdrawal of Vioxx. European Journal of Epidemiology, 20 (2), 127–129. doi: 10.1007/s10654-004-6856-1.CrossRefGoogle Scholar
  14. Cartwright, N. (2007a). Are RCTs the Gold Standard? Biosocieties, 2, 11–20. doi: 10.1017/S1745855207005029.
  15. Cartwright, N. (2008). Evidence-based policy: what’s to be done about relevance? Philosophical Studies, 143(1), 127–136. doi: 10.1007/s11098-008-9311-4.CrossRefGoogle Scholar
  16. Cartwright, N., & Stegenga, J. (2011). A theory of evidence for Evidence-Based policy. In Dawid, P., & Twinning William Vasilaki, M. (Eds.), Evidence, Inference and Enquiry, chap. 11, OUP (pp. 291–322).Google Scholar
  17. Chan, A. W., & Altman, D. G. (2005). Epidemiology and reporting of randomised trials published in PubMed journals. The Lancet, 365(9465), 1159–1162. doi: 10.1016/S0140-6736(05)71879-1.CrossRefGoogle Scholar
  18. Clarke, B., Leuridan, B., & Williamson, J. (2014). Modelling mechanisms with causal cycles. Synthese, 191(8), 1651–1681. doi: 10.1007/s11229-013-0360-7.CrossRefGoogle Scholar
  19. Cohen, M.P. (2016). On three measures of explanatory power with axiomatic representations. British Journal for the Philosophy of Science, 67(4), 1077–1089. doi: 10.1093/bjps/axv017. Early view.CrossRefGoogle Scholar
  20. Craver, C. (2007). Explaining the brain: mechanisms and the mosaic unity of neuroscience. Oxford: Clarendon Press.CrossRefGoogle Scholar
  21. Crupi, V., Chater, N., & Tentori, K. (2013). New axioms for probability and likelihood ratio measures. British Journal for the Philosophy of Science, 64(1), 189–204. doi: 10.1093/bjps/axs018.CrossRefGoogle Scholar
  22. Crupi, V. C., & Tentori, K. (2014). State of the field: measuring information and confirmation. Studies in History and Philosophy of Science Part A, 47, 81–90. doi: 10.1016/j.shpsa.2014.05.002.CrossRefGoogle Scholar
  23. Dardashti, R., Thébaut, K., & Winsberg, E. (2016). Confirmation via analogue simulation: what dumb holes can tell us about gravity. British Journal for the Philosophy of Science. doi: 10.1093/bjps/axv010. Forthcoming.CrossRefGoogle Scholar
  24. Darden, L. (2006). Reasoning in biological discoveries: essays on mechanisms, interfield relations, and anomaly resolution. New York: Cambridge University Press.CrossRefGoogle Scholar
  25. Darwiche, A. (2009). Modeling and reasoning with Bayesian networks. Cambridge University Press.Google Scholar
  26. Dawid, R., Hartmann, S., & Sprenger, J. (2015). The no alternatives argument. British Journal for the Philosophy of Science, 66 (1), 213–234. doi: 10.1093/bjps/axt045.CrossRefGoogle Scholar
  27. Dietrich, F., & Moretti, L. (2005). On coherent sets and the transmission of confirmation. Philosophy of Science, 72(3), 403–424. doi: 10.1086/498471.CrossRefGoogle Scholar
  28. Doll, R., & Peto, R. (1980). Randomised controlled trials and retrospective controls. British Medical Journal, 280, 44. Scholar
  29. Dunn, A. G., Arachi, D., Hudgins, J., Tsafnat, G., Coiera, E., & Bourgeois, F. T. (2014). Financial conflicts of interest and conclusions about neuraminidase inhibitors for influenza. Annals of Internal Medicine, 161(7), 513–518. doi: 10.7326/M14-0933.CrossRefGoogle Scholar
  30. Edwards, W., Lindman, H., & Savage, L. J. (1963). Bayesian statistical inference for psychological research. Psychological Review, 70(3), 193–242. doi: 10.1037/h0044139.CrossRefGoogle Scholar
  31. Eneli, I., Katayoun, S., Camargo, C., & Barr, G.R. (2005). Acetaminophen and the risk of asthma. The epidemiologic and the pathophysiologic evidence. CHEST, 127(2), 604–612. doi: 10.1006/aama.1996.0501.CrossRefGoogle Scholar
  32. Fitelson, B. (2003). A probabilistic theory of coherence. Analysis, 63(279), 194–199. doi: 10.1111/1467-8284.00420.CrossRefGoogle Scholar
  33. Food Drug Administration (2009). Drug induced liver injury: premarketing clinical evaluation - guidance for industry.
  34. Freedman, D. (1997). From association to causation via regression. Advances in Applied Mathematics, 18(1), 59–110. doi: 10.1006/aama.1996.0501.CrossRefGoogle Scholar
  35. Glasziou, P., Chalmers, I., Rawlins, M., & McCulloch, P. (2007). When are randomised trials unnecessary? Picking signal from noise. British Medical Journal, 7589, 349–351. doi: 10.1136/bmj.39070.527986.68.CrossRefGoogle Scholar
  36. Guyatt, G., & et al. (1992). Evidence-based medicine: a new approach to teaching the practice of medicine. Jama, 268(17), 2420–2425. doi: 10.1001/jama.1992.03490170092032.CrossRefGoogle Scholar
  37. Hampson, L. V., Whitehead, J., Eleftheriou, D., & Brogan, P. (2014). Bayesian methods for the design and interpretation of clinical trials in very rare diseases. Statistics in Medicine, 33(24), 4186–4201. doi: 10.1002/sim.6225.CrossRefGoogle Scholar
  38. Heintze, K., & Petersen, K. (2013). The case of drug causation of childhood asthma: antibiotics and paracetamol. European Journal of Clinical Pharmacology, 69 (6), 1197–1209. doi: 10.1007/s00228-012-1463-7.CrossRefGoogle Scholar
  39. Hempel, C. G. (1968). Maximal specificity and lawlikeness in probabilistic explanation. Philosophy of Science, 35(2), 116–133. Scholar
  40. Henderson, A. J., & Shaheen, S. O. (2013). Acetaminophen and asthma. Paediatric Respiratory Review, 14(1), 9–15. doi: 10.1016/j.prrv.2012.04.004.CrossRefGoogle Scholar
  41. Herxheimer, A. (2012). Pharmacovigilance on the turn? Adverse reactions methods in 2012. British Journal of General Practice, 62 (601), 400–401. doi: 10.3399/bjgp12X653453.CrossRefGoogle Scholar
  42. Hesse, M. B. (1952). Operational definition and analogy in physical theories. British Journal for the Philosophy of Science, 2(8), 281–294. Scholar
  43. Hesse, M. B. (1959). On defining analogy. Proceedings of the Aristotelian Society, 60, 79–100. Scholar
  44. Hesse, M. B. (1964). Analogy and confirmation theory. Philosophy of Science, 31(4), 319–327. Scholar
  45. Hill, A. B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300.Google Scholar
  46. Holland, P. W. (1986). Statistics and causal inference. Journal of the American statistical Association, 81(396), 945–960. doi: 10.1080/01621459.1986.10478354.CrossRefGoogle Scholar
  47. Holman, B., & Bruner, J.P. (2015). The problem of intransigently biased agents. Philosophy of Science, 82(5), 956–968. doi: 10.1086/683344.CrossRefGoogle Scholar
  48. Horton, R. (2004). Vioxx, the implosion of Merck, and aftershocks at the FDA. The Lancet, 364(9450), 1995–1996. doi: 10.1016/S0140-6736(04)17523-5.CrossRefGoogle Scholar
  49. Howick, J. (2011). Exposing the vanities - and a qualified defense - of mechanistic reasoning in health care decision making. Philosophy of Science, 78(5), 926–940. doi: 10.1086/662561.CrossRefGoogle Scholar
  50. Howson, C., & Urbach, P. (2006). Scientific Reasoning, 3 edn. Open Court.Google Scholar
  51. Hume, D. (1748). An enquiry concerning human understanding. The University of Adelaide Library 2004 (derived from the Harvard Classics Volume 37, 1910 P.F Collier & Son.)
  52. Ioannidis, J. P. A. (2016). Exposure-wide epidemiology: revisiting Bradford Hill. Statistics in Medicine, 35(11), 1749–1762. doi: 10.1002/sim.6825.CrossRefGoogle Scholar
  53. Johnson, S. R., Tomlinson, G. A., Hawker, G. A., Granton, J. T., & Feldman, B. M. (2010). Methods to elicit beliefs for Bayesian priors: a systematic review. Journal of Clinical Epidemiology, 63(4), 355–369. doi: 10.1016/j.jclinepi.2009.06.003.
  54. Jüni, P., Nartey, L., Reichenbach, S., Sterchi, R., Dieppe, P. A., & Egger, M. (2004). Risk of cardiovascular events and rofecoxib: cumulative meta-analysis. The Lancet, 364(9450), 2021–2029. doi: 10.1016/S0140-6736(04)17514-4.
  55. Kerry, R., Eriksen, T. E., Lie, S. A. N., Mumford, S. D., & Anjum, R. L. (2012). Causation and evidence-based practice: an ontological review. Journal of Evaluation in Clinical Practice, 18(5), 1006–1012. doi: 10.1111/j.1365-2753.2012.01908.x.
  56. Kment, B. (2010). Causation: determination and difference-making. Noûs, 44 (1), 80–111. doi: 10.1111/j.1468-0068.2009.00732.x. Wiley Online Library.
  57. Krumholz, H. M., Ross, J. S., Presler, A. H., & Egilman, D. S. (2007). What have we learnt from Vioxx? British Medical Journal, 334(7585), 120–123. doi: 10.1136/bmj.39070.527986.68.
  58. Kuorikoski, J., Lehtinen, A., & Marchionni, C. (2010). Economic modelling as robustness analysis. The British Journal for the Philosophy of Science, 61(3), 541–567. Scholar
  59. La Caze, A. (2009). Evidence-based medicine must be. Journal of Medicine and Philosophy, 34(5), 509–527. doi: 10.1093/jmp/jhp034.CrossRefGoogle Scholar
  60. La Caze, A., Djulbegovic, B., & Senn, S. (2012). What does randomisation achieve? Evidence-Based Medicine, 17(1), 1–2. doi: 10.1136/ebm.2011.100061.CrossRefGoogle Scholar
  61. LaFollette, H., & Shanks, N. (1995). Two models of models in biomedical research. Philosophical Quarterly, 45(179), 141–160. Scholar
  62. Lamal, P. (1990). On the importance of replication. Journal of Social Behavior and Personality, 5(4), 31–35.Google Scholar
  63. Lewis, D. (1973). Causation. Journal of Philosophy, 70(17), 556–567. Scholar
  64. Lewis, D. (1986). Causal explanation, Philosophical papers, chap. 3. OUP, (Vol. II pp. 214–240).Google Scholar
  65. Lewis, D. (2000). Causation as influence. Journal of Philosophy, 97(4), 182–197. Scholar
  66. Lipton, P. (2003). Inference to the best explanation. Routledge.Google Scholar
  67. Luján, J. L., Todt, O., & Bengoetxea, J. B. (2016). Mechanistic information as evidence in decision-oriented science. Journal for General Philosophy of Science, 47 (2), 293–306. doi: 10.1007/s10838-015-9306-8.CrossRefGoogle Scholar
  68. Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking about mechanisms. Philosophy of Science, 67(1), 1–25. Scholar
  69. Martinez-Gimeno, A., & García-Marcos, L. (2013). The association between acetaminophen and asthma: should its pediatric use be banned? Expert Review of Respiratory Medicine, 7(2), 113–122. doi: 10.1586/ers.13.8.CrossRefGoogle Scholar
  70. McBride, J. T. (2011). The association of acetaminophen and asthma prevalence and severity. Prediatrics, 128(6), 1–5. doi: 10.1186/1745-6215-11-37.CrossRefGoogle Scholar
  71. McGrew, T. (2003). Confirmation, heuristics, and explanatory reasoning, 54 (4), 553–567. doi: 10.1093/bjps/54.4.553.
  72. Meehl, P.E. (1990). Appraising and amending theories: the strategy of lakatosian defense and two principles that warrant it. Psychological Inquiry, 1(2), 108–141. doi: 10.1207/s15327965pli0102_1.CrossRefGoogle Scholar
  73. Mill, J. S. (1884). A system of logic, ratiocinative and inductive: being a connected view of the principles of evidence and the methods of scientific investigation. Longmans, Green and Company.Google Scholar
  74. Moretti, L. (2007). Ways in which coherence is confirmation conducive. Synthese, 157(3), 309–319. doi: 10.1007/s11229-006-9057-5.CrossRefGoogle Scholar
  75. Mumford S., & Anjum, R. L. (2011). Getting causes from powers. Oxford: Oxford University Press.CrossRefGoogle Scholar
  76. Neapolitan, R. E. (2003). Learning Bayesian networks. Pearson.Google Scholar
  77. Open Science Collaboration (2015). Estimating the reproducibility of psychological science. American Heart Journal, 349(6251), 943–aac4716–8. doi: 10.1126/science.aac4716.CrossRefGoogle Scholar
  78. Osimani, B. (2007). Probabilistic information and decision making in the health context: the package leaflet as basis for informed consent. Doctoral Thesis, 1 edn Università della Svizzera Italiana.Google Scholar
  79. Osimani, B. (2013). The precautionary principle in the pharmaceutical domain: a philosophical enquiry into probabilistic reasoning and risk aversion. Health, Risk & Society, 15 (2), 123–143. doi: 10.1080/13698575.2013.771736.CrossRefGoogle Scholar
  80. Osimani, B. (2014a). Causing something to be one way rather than another. Genetic information, causal specificity and the relevance of linear order. Kybernetes, 43(6), 865–881. doi: 10.1108/K-07-2013-0149.
  81. Osimani, B. (2014b). Hunting side effects and explaining them: should we reverse evidence hierarchies upside down? Topoi, 33 (2), 295–312. doi: 10.1007/s11245-013-9194-7.
  82. Osimani, B., & Landes, J. (Forthcoming). Exact replication or varied evidence? The varied of evidence thesis and its methodological implication in medical research.Google Scholar
  83. Osimani, B., & Mignini, F. (2015). Causal assessment of pharmaceutical treatments: why standards of evidence should not be the same for benefits and harms? Drug Safety, 38(1), 1–11. doi: 10.1007/s40264-014-0249-5.CrossRefGoogle Scholar
  84. Osimani, B., Russo, F., & Williamson, J. (2011). Scientific evidence and the law: an objective bayesian formalisation of the precautionary principle in pharmaceutical regulation. Journal of Philosophy, Science and Law, 11.
  85. Papineau, D. (1993). The virtues of randomization. British Journal for the Philosophy of Science, 45(2), 437–450. doi: 10.1093/bjps/45.2.437.CrossRefGoogle Scholar
  86. Pearl, J. (2000). Causality: models, reasoning, and inference, 1st edn. Cambridge University Press.Google Scholar
  87. Platt, J. R. (1964). Strong inference. Science, 146(3642), 347–353. Scholar
  88. Poellinger, R. (2017). Analogy-based inference patterns in pharmacological research, Forthcoming.Google Scholar
  89. Poellinger, R., & Beebe, C. (2017). Bayesian confirmation from analog models, Forthcoming.Google Scholar
  90. Price, K. L., Amy Xia, H., Lakshminarayanan, M., Madigan, D., Manner, D., Scott, J., Stamey, J. D., & Thompson, L. (2014). Bayesian methods for design and analysis of safety trials. Pharmaceutical Statistics, 13 (1), 13–24. doi: 10.1002/pst.1586.CrossRefGoogle Scholar
  91. Revicki, D. A., & Frank, L. (1999). Pharmacoeconomic evaluation in the real world. PharmacoEconomics, 15(5), 423–434. doi: 10.2165/00019053-199915050-00001.CrossRefGoogle Scholar
  92. Roush, S. (2005). Tracking truth: knowledge, evidence, and science. Oxford University Press.Google Scholar
  93. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701. doi: 10.1037/h0037350.CrossRefGoogle Scholar
  94. Rubin, D. B. (2011). Causal inference using potential outcomes. Journal of the American Statistical Association, 81(396), 945–960. doi: 10.1198/016214504000001880.CrossRefGoogle Scholar
  95. Russell, B. (1912). On the notion of cause, Proceedings of the aristotelian society, (Vol. 13 pp. 1–26).
  96. Russo, F., & Williamson, J. (2007). Interpreting causality in the health sciences. International Studies in the Philosophy of Science, 21(2), 157–170. doi: 10.1080/02698590701498084.
  97. Sackett, D. L., Rosenberg, W. M., Gray, J. M., Haynes, R. B., & Richardson, W. S. (1996). Evidence based medicine: what it is and what it isn’t. Bmj, 312(7023), 71–72. doi: 10.1136/bmj.312.7023.71.CrossRefGoogle Scholar
  98. Salmon, W. (1984). Scientific explanation and the causal structure of the world. Princeton: Princeton University Press.Google Scholar
  99. Salmon, W. (1997). Causality and explanation: a reply to two critiques. Philosophy of Science, 64(3), 461–477. Scholar
  100. Schum, D. (2011). Classifying forms and combinations of evidence: Necessary in a science of evidence. In Dawid, P., Twinning, W., & Vasilaki, M. (Eds.), Evidence, inference and enquiry, chap. 2. OUP (pp. 11–36).Google Scholar
  101. Senn, S. (2007). Statistical Issues in Drug Development. Wiley.Google Scholar
  102. Shaheen, S., Potts, J., Gnatiuc, L., Makowska, J., Kowalski, M. L., Joos, G., van Zele, T., van Durme, Y., De Rudder, I., Wöhrl, S., Godnic-Cvar, J., Skadhauge, L., Thomsen, G., Zuberbier, T., Bergmann, K. C., Heinzerling, L., Gjomarkaj, M., Bruno, A., Pace, E., Bonini, S., Fokkens, W., Weersink, E. J. M., Loureiro, C., Todo-Bom, A., Villanueva, C. M., Sanjuas, C., Zock, J. P., Janson, C., & Burney, P. (2008). The relation between paracetamol use and asthma: a ga2len european case-control study. European Respiratory Journal, 32(5), 1231–1236. doi: 10.1183/09031936.00039208.
  103. Shaheen, S., Sterne, J., Songhurst, C., & Burney, P. (2000). Frequent paracetamol use and asthma in adults. Thorax, 55(4), 266–270. doi: 10.1136/thorax.55.4.266.CrossRefGoogle Scholar
  104. Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, prediction, and search. Adaptive computation and machine learning. MIT Press.Google Scholar
  105. Steel, D. (2008). Across the boundaries. Extrapolation in biology and social sciences. Oxford University Press.Google Scholar
  106. Stegenga, J. (2011). Is meta-analysis the platinum standard of evidence? Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 42(4), 497–507. doi: 10.1016/j.shpsc.2011.07.003.CrossRefGoogle Scholar
  107. Stegenga, J. (2014). Down with the hierarchies. Topoi, 33(2), 313–322. doi: 10.1007/s11245-013-9189-4.CrossRefGoogle Scholar
  108. Stegenga, J. (2015). Measuring effectiveness. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 54, 62–71. doi: 10.1016/j.shpsc.2015.06.003.CrossRefGoogle Scholar
  109. Straus, S. E., & McAlister, F. A. (2000). Evidence-based medicine: a commentary on common criticisms. Canadian Medical Association Journal, 163(7), 837–841.Google Scholar
  110. Suppes, P. (Ed.) (1970). A Probabilistic Theory of causality. North-Holland Pub. Co. Google Scholar
  111. Teira, D. (2011). Frequentist versus bayesian clinical trials. In Gifford, F. (Ed.) Handbook of Philosophy of Medicine (pp. 255–298). Wiley.Google Scholar
  112. Teira, D., & Reiss, J. (2013). Causality, impartiality and evidence-based policy, Mechanism and causality in biology and economics, (pp. 207–224). Springer.Google Scholar
  113. Tillman, R. E., & Eberhardt, F. (2014). Learning causal structure from multiple datasets with similar variable sets. Behaviormetrika, 41(1), 41–64. doi: 10.2333/bhmk.41.41.CrossRefGoogle Scholar
  114. Unruh, W. G. (2008). Dumb holes: analogues for black holes. Philosophical Transactions of The Royal Society A, 366, 2905–2913. doi: 10.1098/rsta.2008.0062.CrossRefGoogle Scholar
  115. Upshur, R. (1995). Looking for rules in a world of exceptions: reflections on evidence-based practice. Perspectives in Biology and Medicine, 48(4), 477–489. doi: 10.1353/pbm.2005.0098.CrossRefGoogle Scholar
  116. Vandenbroucke, J. P., Broadbent, A., & Pearce, N. (2016). Causality and causal inference in epidemiology: the need for a pluralistic approach. International Journal of Epidemiology. doi: 10.1093/ije/dyv341.CrossRefGoogle Scholar
  117. Waters, K. C. (2007). Causes that make a difference. Journal of Philosophy, 104(11), 551–579. Scholar
  118. Weatherall, S., Ioannides, S., Braithwaite, I., & Beasley, R. (2014). The association between paracetamol use and asthma: causation or coincidence? Clinical et Experimental Allergy, 45, 108–113. doi: 10.1111/cea.12410.CrossRefGoogle Scholar
  119. Weber, M. (2006). The central dogma as a thesis of causal specificity. History and Philosophy of the Life Sciences, 28(4), 595–609. Scholar
  120. Weed, D. L. (2005). Weight of evidence: a review of concept and methods. Risk Analysis, 25(6), 1545–1557. doi: 10.1111/j.1539-6924.2005.00699.x.CrossRefGoogle Scholar
  121. Weisberg, J. (2015). You’ve come a long way, bayesians. Journal of Philosophical Logic, 44(6), 817–834. doi: 10.1007/s10992-015-9363-9.CrossRefGoogle Scholar
  122. Wheeler, G., & Scheines, R. (2013). Coherence and confirmation through causation. Mind, 122(485), 135–170. doi: 10.1093/mind/fzt019.CrossRefGoogle Scholar
  123. Wimsatt, W. C. (1981). Robustness, reliability and overdetermination. In Brewer, M., & Colllins, B. (Eds.), Scientific inquiry and the social sciences: festschrift for Donald Campbell, (pp. 125–163). Jossey-Bass Publishers.Google Scholar
  124. Wimsatt, W.C. (2012). Robustness, reliability, and overdetermination (1981). In Soler, L., Trizio, E., Nickles, T., & Wimsatt, W. (Eds.), Characterizing the robustness of science, boston studies in the philosophy of science, (Vol. 292 pp. 61–87): Springer, DOI doi: 10.1007/978-94-007-2759-5_2.
  125. Woodward, J. (2003). Making things happen: a theory of causal explanation (Oxford Studies in the Philosophy of Science). Oxford University Press.Google Scholar
  126. Woodward, J. (2006). Some varieties of robustness. Journal of Economic Methodology, 13(2), 219–240. doi: 10.1080/13501780600733376.CrossRefGoogle Scholar
  127. Woodward, J. (2010). Causation in biology: stability, specificity and the choice of levels of explanation. Biology and Philosophy, 44, 267–318. doi: 10.1007/s10539-010-9200-z.
  128. Worrall, J. (2007a). Evidence in medicine and evidence-based medicine. Philosophy Compass, 2(6), 981–1022. doi: 10.1111/j.1747-9991.2007.00106.x.
  129. Worrall, J. (2007b). Why there’s no cause to randomize. British Journal for the Philosophy of Science, 58(3), 451–488. doi: 10.1093/bjps/axm024.
  130. Worrall, J. (2010). Do we need some large, simple randomized trials in medicine? In Suárez, M., Dorato, M., & Rédei, M. (Eds.), EPSA Philosophical issues in science: Launch of the European Philosophy of Science Association. doi: 10.1007/978-90-481-3252-2_27 (pp. 289–301).CrossRefGoogle Scholar
  131. Xie, L., Li, J., Xie, L., & Bourne, P. (2009). Drug discovery using chemical systems biology: identification of the protein-ligand binding network to explain the side effects of CETP inhibitors. PLos Computational Biology, 5(5), 1–12. doi: 10.1371/journal.pcbi.1000387.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Jürgen Landes
    • 1
  • Barbara Osimani
    • 1
    Email author
  • Roland Poellinger
    • 1
  1. 1.Munich Center for Mathematical PhilosophyLMU MunichMunichGermany

Personalised recommendations