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, and the proposed solution of Karl Popper.
   • 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, with 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.
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 and economic 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 Methododology of Positive Economics. Reprinted in M&M, page 647-660.
   • * 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.
   • Simonsohn, Uri; Nelson, Leif D.; Simmons, Joseph P. (2014). J. P-Curve: A Key to the File Drawer, Journal of Experimental Psychology: General, 143(2), 534-547.
   • * Christensen, Garret, and Edward Miguel (2018). Transparency, Reproducibility, and the Credibility of Economics Research. Journal of Economic Literature, 56(3): 920-80.
   • Dale J. Poirier (1988). Frequentist and Subjectivist Perspectives on the Problems of Model Building in Economics. Journal of Economic Perspectives, 2(1): 121-144.