How do we know, or think we know, that smoking causes lung cancer? How do we know, or think we know, that even small amounts of lead can cause damage to a child's brain? How can we scientifically investigate whether tougher prison sentences really reduce crime? This course is about the science of reasoning with and establishing causal claims of this sort, especially where they involve statistical data.  Although quite alot is now known about causal reasoning, the great bulk of it has been discovered only in the last 15 years. Unfortunately, there are virtually no textbooks or reasonable readings for undergraduates on this topic. As a result, several colleagues and I are striving to develop a web-based textbook that includes hands-on interactive modules, simulated causal systems and simulated laboratories, and in this course we will use the first version of it. All of the concepts will be taught qualitatively, and no statistics background is required.

Starting Sept. 1^st, you will do lessons on the web almost every week. On Wednesdays we will meet in a computer cluster (Baker 140-C) and I will help as you work through some of the web-based material.  We will gather on Monday to discuss the lessons, work through any questions you have on content, and systematically critique the utility of the web-based material as instructional devices. Over 20 universities in several countries are committed to use this material, and you will helping to develop it.
 

You will be graded on performance in three areas:

50% : Class attendance and participation

30% : Completing web-based exercises

20% : 4 Quizes & Regular homework assignments

Quizes will be short and relatively easy, and announced at least a week in advance. There will be no final exam and no final paper.

  Week 1
 

Week 2

Week 3
 

Week 4
 

Week 5
 

Week 6
 

Week 7
 

Week 8
 

Week 9
 

Week 10
 

Week 11
 

Week 12
 

Week 13
 

Week 14
 

Week 15
 

Assignment 1: Due Wednesday, August 25^th: Find two current event stories involving "studies" about a causal claim. You may use a current newspaper (e.g. the Pgh Post-Gazette or the NY Times), or a news magazine (e.g., Time, Newsweek, U.S. News and World Report, etc.). For each story, write a sentence or two summarizing the causal claim, and a short paragraph describing the evidence for the claim. Turn in your assignment on a typed sheet, and make sure to include your name.

Assignment 2: Due Monday, August 30^th: Describe two causal questions of societal importance that you would like to learn more about. For example, you might be curious as to whether the prevalence of violent movies and/or computer games really causes more violence in society. In a short paragraph, describe each question and your hypothesis about the truth of the matter. Again, turn in your assignment at the beginning of class on a typed sheet, and make sure to include your name.

Assignment 3: Due Wednesday, Sept. 1: Take each of the causal questions that you identified in assignment 2, and use the library, and in particular the skills for searching online databases you learned at Hunt Library, to identify 5 sources on each question that you might pursue to learn more about research that has already been conducted on each question. Give the full citation for each source. If there is an abstract, summarize it in a sentence or two.

 Assignment 4: Due Wednesday, Sept. 8th: Get the text for the sources you cited in assignment 3, and read each. Take the best source for one of the questions (the one that has the clearest claims and connection with measured data) and do the following. 1) Identify all the causal claims and questions that occur in the piece. 2) Identify the causal variables involved in these claims, and the values that these variables plausibly range over. 3) Identify the measured variables actually used. Turn in the source along with your typed up answer.

Ehring, D. (1982) "Causal Asymmetry," Journal of Philosophy 79, 761-64

Hausman, D. (1984). "Causal priority." Nous 18, 261-279.

Papineau, D. (1985) "Causal Asymmetry," British Journal for Philosophy of Science, 36, 273-89.

Papineau, D. (1985) "Can we Reduce Causal Direction to Probabilities?," PSA 1992, vol. 2, 238-52.

Simon, H. (1953). Causal ordering and identifiability. Studies in Econometric Methods. Hood and Koopmans (eds). 49-74.Wiley, NY.
 
 

Davidson, D. (1980). Essays on Actions, Reasons and Causes, Oxford

Kim, J. (1971). "Causes and Events: Mackie on Causation" reprinted in E. Sosa, Causation and Conditionals (Oxford, 1975).

Kim, J. (1973). "Causation, Nomic Subsumption and the Concept of Event," Journal of Philosophy 70, 217-36.
 
 

Kim, J. (1973). "Causes and Counterfactuals," J. of Phil., 70, reprinted in Sosa, Causation and Conditionals.

Lewis, D. (1973a). Counterfactuals. Harvard University Press, Cambridge, MA.

Lewis, D. (1973b). Causation. Journal of Philosophy 70, 556-572.

Stalnaker, R. "A Theory of Conditionals," in N. Rescher, ed. Studies in Logical Theory, reprinted in E. Sosa, Causation and Conditionals (Oxford, 1975).
 
 

Bollen, Kenneth A. (1989). Structural Equations with Latent Variables. Wiley, 1989

Blalock, H. (Ed.) (1971). Causal Models in the Social Sciences. Aldine-Atherton, Chicago.

Granger, C. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424-438.

Heise, D. (1975). Causal Analysis. Wiley, New York

Kenny, D. (1979). Correlation and Causality. Wiley, New York.

Simon, H. (1954).Spurious correlation: a causal interpretation. JASA. 49, 467-479.

Wright, S. (1934). The method of path coefficients. Ann. Math. Stat. 5, 161-215.
 
 

Ducass, C.J. "On the Nature and Observability of the Causal Relation," Journal of Philosophy 23, reprinted in E. Sosa, ed., Causation and Conditionals

David Hume, An Inquiry Concerning Human Understanding, Hackett

Mackie, J. (1974). The Cement of the Universe. Oxford University Press, New York.
 
 

Cartwright, N. (1983). How the Laws of Physics Lie. Oxford University Press, New York

Cartwright, N. (1989). Nature's Capacities and Their Measurement. Clarendon Press, Oxford.

Davis, W. (1988). Probabilistic theories of causation. Probability and Causality, James Fetzer (ed.). D. Reidel, Dordrecht

Elby, A. (1992) Should we explain the EPR causally? Philosophy of Science 59, 16-25.

Meek, C., and Glymour, C. (1994). "Conditioning and intervening," British Journal for the Philosophy of Science.

Pearl, Judea, and Thomas Verma (1993). "A Theory of Inferred Causation." Principles of Knowledge Representation and Reasoning: Proceeding of the Second International Conference, J.A. Allen, R. Fikes, and E. Sandewall, eds. Morgan Kaufmann, San Mateo, CA, pp. 441-452.

Reichenbach, H. (1956). The Direction of Time. Univ. of California Press, Berkeley, CA.

Salmon, W. (1980). Probabilistic causality. Pacific Philosophical Quarterly 61, 50-74.

Shafer, Glenn (1995). The Art of Causal Conjecture. MIT Press.

Suppes, P. (1970). A Probabilistic Theory of Causality. North-Holland, Amsterdam.
 
 

Hempel, C. (1965). Aspects of Scientific Explanation, Free Press, (sections 1,2, and 4).

Humphreys, P. (1989). The Chances of Explanation. Princeton Univ. Press.

Kitcher, P. (1989). "Explanatory Unification and the Causal Structure of the World" in P. Kitcher and W. Salmon, eds. Minnesota Studies in the Philosophy of Science. vol 13. Scientific Explanation. University of Minnesota Press pp. 410-505.

Salmon, Wesley C. (1984). Scientific Explanation and the Causal Structure of the World. Princeton University Press.

Sanford, D. (1976). "The Direction of Causation and the Direction of Conditionship," Journal of Philosophy, 73: 193-207.
 
 

Cooper, G., & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 308-347.

Holland, P. (1986). Statistics and causal inference. JASA 81, 945-960.

Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. Morgan and Kaufman, San Mateo

Robins, James (1987). "A Graphical Approach to the Identification and Estimation of Causal Parameters in Mortality Studies with Sustained Exposure Periods." Journal of Chronic Diseases 40, Supplement 2 139S-161S.

Scheines, R. (1996). "An Introduction to Causal Inference," in Causality in Crisis?, ed. V. Mckim and S. Turner (eds.), Univ. of Notre Dame Press.

Spirtes, Peter, Clark Glymour, and Richard Scheines (1993). Causation, Prediction, and Search. Springer-Verlag Lecture Notes in Statistics, No. 81.

Spirtes, P., Meek, C., and Richardson, T. (1995). Causal inference in the presence of latent variables and selection bias, in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, ed P. Besnard and S. Hanks, Morgan Kaufmann Publishers, Inc., San Mateo, pp 499-506.
 
 

Fisher, R. (1951). The Design of Experiments. Oliver and Boyd, Edinburgh.

Kadane, J. and Seidenfeld, T. (1992). Statistical issues in the analysis of data gathered in the new designs. Toward a More Ethical Clinical Trial, J. Kadane (ed.), John Wiley & Sons, NY, forthcoming.

Rubin, D. (1978). Bayesian inference for causal effects: The role of randomizations. Ann. Stat. 6, 34-58.

Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology 66, 688-701.

Spirtes, Peter, Clark Glymour, and Richard Scheines (1993). Causation, Prediction, and Search. Springer-Verlag Lecture Notes in Statistics, No. 81. (chapter 8)
 
 

Gasking, D. (1955). "Causation and Recipes," Mind, 64, reprinted in M. Brand, ed. The Nature of Causation (Urbana, 1976).

Hausman, D. (1986) "Causation and Experimentation," American Philosophical Quarterly, 23, 143-154.

Kelly, K. (1996). The Logic of Reliable Inquiry. Oxford University Press (chapter 14).

Spirtes, P., Glymour, C., and Scheines, R. (1994) "Inference, Intervention, and Prediction", in Selecting Models from Data: Artificial Intelligence and Statistics IV, ed. by P. Cheesman and R. Oldford, Springer Verlag, Lecture Notes in Statistics 89, pp. 215-223.

von Wright, G.H. (1975) Causality and Determinism, Columbia Univ. Press.