Topics and readings

These topics won’t map cleanly into lectures, but it summarizes what we’ll aim to cover for this part of the course.

For readings not linked to below, I’ll do my best to make the readings available at the start of the course.

1. Why do we need a philosophy of science? The practical and political problem of “demarcation.”

   • Ben Goldacre (2008). “The Doctor will sue you now”, Chapter 10 in Bad Science.
   • Alan Sokal (1996). Transgressing the boundaries: Toward a transformative hermenutics of quantum gravity. Social Text 46/47: 217-252.
   • Alan Sokal (1996). Transgressing the Boundaries: An Afterword. Dissent 43(4): 93-99.
   • John Baez (2006). The Bogdanoff Affair.

2. Popper and some classical challenges

   • Samir Okasha (2002). Philosophy of Science: A Very Short Introduction. Oxford University Press.
   • Larry Laudan (1983). “The Demise of the Demarcation Problem.” In Cohen, R. S. & Laudan, L. (Eds.), Physics, Philosophy and Psychoanalysis: Essays in Honor of Adolf Grünbaum, D. Reidel Publishing Company, 111-127.
   • H. M. Collins (1983). The Sociology of Scientific Knowledge: Studies of Contemporary Science. Annual Review of Sociology, 9: 265-285.
   • Itai Yanai and Martin J. Lercher (2021). Disputes in modern science are settled with empiricism alone, an approach early scholars would have questioned [bookreview], Science 371(6524): 37.

3. Scientific explanations

   • Joseph Heath (2005). Methodological Individualism. The Stanford Encyclopedia of Philosophy (Spring 2005 Edition), Edward N. Zalta (ed.)
   • Emile Durkheim (1938). Social Facts. Reprinted in M&M, page 433-440.
   • Kevin Hoover (2009) Microfoundations and the Ontology of Macroeconomics. in Harold Kincaid and Donald Ross, editors, Oxford Handbook of the Philosoph of Economic Science. Oxford: Oxford University Press, 2009; ch. 14, pp. 386-409.

4. Causality: Economic and Statistical explanations

   • The JASA debate on causality
     • Paul W. Holland (1986). Statistics and Causal Inference. Journal of American Statistical Association, 81(396): 945-960.
     • Donald B. Rubin (1986). Statistics and Causal Inference: Comment: Which Ifs Have Causal Answers. Journal of American Statistical Association, 81(396): 961-962.
     • D. R. Cox (1986). Statistics and Causal Inference: Comment. Journal of American Statistical Association, 81(396): 963-964.
     • Clark Glymour (1986). Statistics and Causal Inference: Comment: Statistics and Metaphysics. Journal of American Statistical Association, 81(396): 964-966.
     • Clive Granger (1986). Statistics and Causal Inference: Comment. Journal of American Statistical Association, 81(396): 967-968.
     • Paul W. Holland (1986). Statistics and Causal Inference: Rejoinder. Journal of American Statistical Association, 81(396): 967-968.
   • James J. Heckman (2008). Econometric Causality International Statistical Review, 76(1): 1-27.

5. Statistical and scientific practice

   • Milton Friedman (1953). The Methodology of Positive Economics. Excerpt from Essays in Positive Economics, University of Chicago Press. Reprinted in Readings in The Philosophy of Social Science (ed: Michael Martin and Lee C. McIntyre, MIT Press, 1994), page 647-660.
   • Dale J. Poirier (1988). Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics. Journal of Economic Perspectives, 2(1): 121-144.
   • John H. Cochrane (1989). The Sensitivity of Tests of the Intertemporal Allocation of Consumption to Near-Rational Alternatives. American Economic Review, 79(3): 319-337.
   • John P.A. Ioannidis (2005). Why Most Published Research Findings Are False. PLoS Med, 2(8): 696-701.
   • Abel Brodeur, Mathias Lé, Marc Sangnier and Yanos Zylberberg (2016). Star Wars: The Empirics Strike Back, American Economic Journal: Applied Economics, 8(1): 1-32.
   • Christensen, Garret, and Edward Miguel (2018). Transparency, Reproducibility, and the Credibility of Economics Research. Journal of Economic Literature, 56(3): 920-80.