References
Ampère, J.-M. (1826/2012). Mathematical Theory of Electro-Dynamic Phenomena Uniquely Derived from Experiments, transl. M. D. Godfrey. Paris: A. Hermann, archive.org/details/AmpereTheorieEn. Bacon, F. (1620/1994). Novum Organum. Chicago: Open Court.
Baumgartner, M., & Falk, C. (2019). Boolean difference-making: A modern regularity theory of causation. The British Journal for the Philosophy of Science, https://doi.org/10.1093/bjps/axz047. Baumgartner, M., & Graßhoff, G. (2003). Kausalität und kausales Schliessen. Bern: Bern Studies in the History and Philosophy of Science.
Bellman, R. E. (1961). Adaptive Control Processes: A Guided Tour. Princeton: Princeton University Press.
Bergadano, F. (1993). Machine learning and the foundations of inductive inference. Minds and Machines, 3, 31–51.
Bird, A. (2010). Eliminative abduction: Examples from medicine. Studies in History and Philosophy of Science Part A, 41(4), 345–52.
Bogen, J., & Woodward, J. (1988). Saving the phenomena. The Philosophical Review, 97(3), 303–52.
boyd, , d., & Crawford, K. (2012). Critical questions for big data. Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–79.
Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231.
Burian, R. (1997). Exploratory experimentation and the role of histochemical techniques in the work of Jean Brachet, 1938–1952. History and Philosophy of the Life Sciences, 19, 27–45.
Calhoun, C. (2002). Dictionary of the Social Sciences. Oxford: Oxford University Press.
Callebaut, W. (2012). Scientific perspectivism: A philosopher of science’s response to the challenge of big data biology. Studies in History and Philosophy of Biological and Biomedical Science, 43(1), 69–80.
Calude, C. S., & Longo, G. (2017). The deluge of spurious correlations in big data. Foundations of Science, 22(3), 595–612.
Cartwright, N. (1979). Causal laws and effective strategies. Noûs, 13(4), 419–37.
Cartwright, N. (1983). How the Laws of Physics Lie. Oxford: Oxford University Press.
Clark, A. (1996). Philosophical Foundations. In Boden, M. A., ed., Artificial Intelligence. San Diego, CA: Academic Press, pp. 1–22.
Colman, A. M. (2015). Oxford Dictionary of Psychology. Oxford: Oxford University Press.
Coveney, P. V., Dougherty, E. R., & Highfield, R. R. (2016). Big data needs big theory too. Philosophical Transactions of the Royal Society A, 374, 20160153.
Duhem, P. (1906/1962). The Aim and Structure of Physical Theory. New York: Atheneum.
Einstein, A. (1934). On the method of theoretical physics. Philosophy of Science, 1(2), 163–9.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115–18.
Feest, U., & Steinle, F. (2016). Experiment. In Hymphreys, P., ed., The Oxford Handbook of Philosophy of Science. Oxford: Oxford University Press, pp. 274–95.
Feynman, R. (1974). Cargo cult science. Engineering and Science, 37(7), 10–13.
Flach, P. (2012). Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge: Cambridge University Press.
Floridi, L. (2008). Data. In Darity, W. A., ed., International Encyclopedia of the Social Sciences. Detroit: Macmillan.
Floridi, L. (2011). The Philosophy of Information. Oxford: Oxford University Press.
Floridi, L. (2019). Semantic conceptions of information. In E. N. Zalta, ed., The Stanford Encyclopedia of Philosophy (Winter 2019 Edition), plato.stanford.edu/archives/win2019/entries/information-semantic/.
Foster, I., Ghani, R., Jarmin, R. S., Kreuter, F., & Lane, J. (2017). Big Data and Social Science. Boca Raton, FL: CRC Press.
Foster, I., & Heus, P. (2017). Databases. In Foster, I, Ghani, R, Jarmin, R. S, Kreuter, F, & Lane, J, eds., Big Data and Social Science. Boca Raton, FL: CRC Press, pp. 93–124.
Frické, M. (2014). Big data and its epistemology. Journal of the Association for Information Science and Technology, 66(4), 651–61.
Ghani, R., & Schierholz, M. (2017). Machine learning. In Foster, I, Ghani, R, Jarmin, R. S, Kreuter, F, & Lane, J, eds., Big Data and Social Science. Boca Raton, FL: CRC Press, pp. 147–86.
Gillies, D. (1996). Artificial Intelligence and Scientific Method. Oxford: Oxford University Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: Massachusetts Institute of Technology Press.
Graßhoff, G., & May, M. (2001). Causal regularities. In Spohn, W., Ledwig, M., & Esfeld, M., eds., Current Issues in Causation. Paderborn: Mentis Verlag, pp. 85–114.
Hacking, I. (1992). The self-vindication of the laboratory sciences. In Pickering, A., ed., Science as Practice and Culture. Chicago: Chicago University Press, pp. 29–64.
Harman, G., & Kulkarni, S. (2007). Reliable Reasoning. Induction and Statistical Learning Theory. Boston: Massachusetts Institute of Technology Press.
Hastie, T., Tibshirani, R., & Friedman, J. (2001). The Elements of Statistical Learning. New York: Springer.
Heisenberg, W. (1931). Kausalgesetz und Quantenmechanik. Erkenntnis, 2, 172–82.
Hempel, C. G. (1966). Philosophy of Natural Science. Upper Saddle River, NJ: Prentice Hall.
Höfer, T., Przyrembel, H., & Verleger, S. (2004). New evidence for the theory of the stork. Paediatric and Perinatal Epidemiology, 18(1), 88–92.
Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81(396), 945–60.
Hosni, H., & Vulpiani, A. (2018a). Forecasting in light of big data. Philosophy & Technology, 31, 557–69.
Hosni, H., & Vulpiani, A. (2018b). Data science and the art of modelling. Lettera Matematica, 6, 121–9.
Hume, D. (1748). An Enquiry Concerning Human Understanding. London: A. Millar.
Jelinek, F. (2009). The dawn of statistical ASR and MT. Computational Linguistics, 35(4), 483–94.
Keynes, J. M. (1921). A Treatise on Probability. London: Macmillan.
Kitchin, R. (2014). The Data Revolution. Los Angeles: Sage.
Knüsel, B., Zumwald, M., Baumberger, C., Hirsch Hadorn, G., Fischer, E., Bresch, D., & Knutti, R. (2019). Applying big data beyond small problems in climate research. Nature Climate Change, 9, 196–202.
Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge: Cambridge University Press.
Kuhlmann, M. (2011). Mechanisms in dynamically complex systems. In Illari, P., Russo, F., & Williamson, J., eds., Causality in the Sciences. Oxford: Oxford University Press.
Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. Research Report. blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
Lavoisier, A. (1789/1890). Elements of Chemistry. Edinburgh: William Creech.
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6167), 1203–5.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature 521, 436–44.
Leonelli, S. (2014). What difference does quantity make? On the epistemology of big data in biology. Big Data & Society 1(1).
Leonelli, S. (2016). Data-Centric Biology: A Philosophical Study, Chicago: Chicago University Press.
Leonelli, S. (2019). What distinguishes data from models? European Journal for Philosophy of Science 9, 22.
Luca, M., & Bazerman, M. H. (2020). Power of Experiments: Decision Making in a Data-Driven World. Cambridge, MA: Massachusetts Institute of Technology Press.
Lyon, A. (2016). Data. In Humphreys, P., ed., The Oxford Handbook of Philosophy of Science. Oxford: Oxford University Press.
Mach, E. (1905/1976). Knowledge and Error: Sketches on the Psychology of Enquiry. Dordrecht: D. Reidel.
Mach, E. (1923/1986). Principles of the Theory of Heat – Historically and Critically Elucidated, transl. T. J. McCormack. Dordrecht: D. Reidel.
Mackie, J. L. (1967). Mill’s methods of induction. In Edward, P., ed., The Encyclopedia of Philosophy, Vol. 5. New York: MacMillan, pp. 324–32.
Mackie, J. L. (1980). The Cement of the Universe. Oxford: Clarendon Press.
Mayer-Schönberger, V., & Cukier, K. (2013). Big Data. London: John Murray.
Mazzocchi, F. (2015). Could big data be the end of theory in science? A few remarks on the epistemology of data-driven science. EMBO Reports, 16(10), 1250–5.
Mill, J. S. (1886). System of Logic. London: Longmans, Green & Co.
Minsky, M. L., & Papert, S. A. (1969). Perceptrons. An Introduction to Computational Geometry. Cambridge: Massachusetts Institute of Technology Press.
Napoletani, D., Panza, M., & Struppa, D. C. (2011). Toward a philosophy of data analysis. Foundations of Science, 16(1), 1–20.
Ng, A., & Soo, K. (2017). Numsense! Data Science for the Layman. Seattle, WA: Amazon.
Northcott, R. (2019). Big data and prediction: Four case studies. Studies in History and Philosophy of Science A. doi:10.1016/j.shpsa.2019.09.002
Norton, J. D. (1995). Eliminative induction as a method of discovery: Einstein’s discovery of General Relativity. In Leplin, J., ed., The Creation of Ideas in Physics: Studies for a Methodology of Theory Construction. Dordrecht: Kluwer Academic Publishers, pp. 29–69.
Norton, J. D. (2005). A little survey of induction. In Achinstein, P., ed., Scientific Evidence: Philosophical Theories and Applications. Baltimore: Johns Hopkins University Press, pp. 9–34.
Norton, J. D. (2007). Causation as folk science. Philosophers’ Imprint, 3, 4.
Norvig, P. (2009). Natural language corpus data. In Segaran, T & Hammerbacher, J, eds., Beautiful Data. Sebastopol, CA: O’Reilly, pp. 219–42.
Panza, M., Napoletani, D., & Struppa, D. (2011). Agnostic science. Towards a philosophy of data analysis. Foundations of Science, 16(1), 1–20.
Pearson, K. (1911). The Grammar of Science, 3rd ed., Black.
Pietsch, W. (2014). The structure of causal evidence based on eliminative induction. Topoi, 33(2), 421–35.
Pietsch, W. (2015). Aspects of theory-ladenness in data-intensive science. Philosophy of Science 82(5): 905–16.
Pietsch, W. (2016a). The causal nature of modeling with big data. Philosophy & Technology, 29(2), 137–71.
Pietsch, W. (2016b). A difference-making account of causation, philsci-archive.pitt.edu/11913/.
Pietsch, W. (2017). Causation, probability, and all that: Data science as a novel inductive paradigm. In Dehmer, M & Emmert-Streib, F, eds., Frontiers in Data Science. Boca Raton, FL: CRC Press, pp. 329–53.
Pietsch, W. (2019). A causal approach to analogy. Journal for General Philosophy of Science, 50(4), 489–520.
Plantin, J. C., & Russo, F. (2016). D’abord les données, ensuite la méthode? Big data et déterminisme en sciences sociales. Socio, 6, 97–115.
Popper, K. (1935/2002). The Logic of Scientific Discovery. London: Routledge Classics.
Ratti, E. (2015). Big data biology: Between eliminative inferences and exploratory experiments. Philosophy of Science, 82(2), 198–218.
Rheinberger, H.-J. (2011). Infra-experimentality: From traces to data, from data to patterning facts. History of Science, 49(3), 337–48.
Rosenblatt, F. (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Washington, DC: Spartan Books.
Russell, B. (1913). On the notion of cause. Proceedings of the Aristotelian Society, 13, 1–26.
Russell, S., & Norvig, P. (2009). Artificial Intelligence. Upper Saddle River, NJ: Pearson.
Russo, F. (2007). The rationale of variation in methodological and evidential pluralism. Philosophica, 77, 97–124.
Russo, F. (2009). Causality and Causal Modelling in the Social Sciences. Measuring Variations, New York: Springer.
Scholl, R. (2013). Causal inference, mechanisms, and the Semmelweis case. Studies in History and Philosophy of Science Part A, 44(1), 66–76.
Schurz, G. (2014). Philosophy of Science: A Unified Approach, New York, NY: Routledge.
Solomonoff, R. (1964a). A formal theory of inductive inference, part I. Information and Control, 7(1), 1–22.
Solomonoff, R. (1964b). A formal theory of inductive inference, part II. Information and Control, 7(2), 224–54.
Solomonoff, R. (1999). Two kinds of probabilistic induction. The Computer Journal, 42(4), 256–9.
Solomonoff, R. (2008). Three kinds of probabilistic induction: Universal distributions and convergence theorems. The Computer Journal, 51(5), 566–70.
Steinle, F. (1997). Entering new fields: Exploratory uses of experimentation. Philosophy of Science 64, S65–S74.
Sterkenburg, T. F. (2016). Solomonoff prediction and Occam’s Razor. Philosophy of Science 83(4), 459–79.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988–99.
Vapnik, V. N. (2000). The Nature of Statistical Learning Theory, 2nd ed., New York: Springer.
Vickers, J. (2018). The problem of induction. In E. N. Zalta, ed., The Stanford Encyclopedia of Philosophy (Spring 2018 Edition), plato.stanford.edu/archives/spr2018/entries/induction-problem/.
Vo, H., & Silva, C. (2017). Programming with Big Data. In Foster, I, Ghani, R, Jarmin, R. S, Kreuter, F, & Lane, J, eds., Big Data and Social Science. Boca Raton, FL: CRC Press, pp. 125–44.
Wan, C., Wang, L., & Phoha, V. (2019). A survey on gait recognition. ACM Computing Surveys, 51(5), 89.
Wheeler, G. (2016). Machine epistemology and big data. In McIntyre, L. & Rosenberg, A., eds., The Routledge Companion to Philosophy of Social Science. London: Routledge.
Williamson, J. (2004). A dynamic interaction between machine learning and the philosophy of science. Minds and Machines, 14(4), 539–49.
Williamson, J. (2009). The philosophy of science and its relation to machine learning. In Gaber, M. M., ed., Scientific Data Mining and Knowledge Discovery: Principles and Foundations. Berlin: Springer, pp. 77–89.
Woodward, J. (2011). Data and phenomena: A restatement and a defense. Synthese, 182, 165–79.
von Wright, G. H. (1951). A Treatise on Induction and Probability. New York: Routledge.
Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. 2016. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nature Communications, 7, 12474.
Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In Fleet, D, Pajdla, T, Schiele, B, & Tuytelaars, T, eds., Computer Vision – ECCV 2014. New York, NY: Springer, pp. 818–33.