Interventions and causal inference

Philosophy of Science

Volumn 74, Issue 5, 2007, Pages 981-995

  Eberhardt, Frederick ^a   Scheines, Richard ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 46649113945     PISSN: 00318248     EISSN: None     Source Type: Journal    
DOI: 10.1086/525638     Document Type: Conference Paper

References (17)

1. Campbell, J. (2007), "An Interventionist Approach to Causation in Psychology", in A. Gopnik and L. Schulz (eds.), Causal Learning: Psychology and Computation. Oxford: Oxford University Press.
2. Cartwright, Nancy (2001), "What is Wrong with Bayes Nets?", The Monist. 16: 242-264.
3. _ (2002), "Against Modularity, the Causal Markov Condition and Any Link between the Two", British Journal of Philosophy of Science 53: 411-453.
4. _ (2006), "From Metaphysics to Method: Comments on Manipulability and the Causal Markov Condition", British Journal of Philosophy of Science 57: 197-218.
5. Eberhardt, Frederick, Clark Glymour, and Richard Scheines (2005), "On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations among N Variables", in Fahiem Bacchus and Tommi Jaakkola (eds.), Proceedings of the 21st Conference on Uncertainty and Artificial Intelligence. Corvallis, OR: AUAI Press, 178-184.
6. _ (2006), "N-1 Experiments Suffice to Determine the Causal Relations among N Variables", in Dawn E. Holmes and Lakhmi C. Jain (eds.), Innovations in Machine Learning, Theory and Applications Series: Studies in Fuzziness and Soft Computing. Vol. 194. New York: Springer-Verlag.
7. Fisher, Ronald A. (1935), The Design of Experiments. New York: Hafner.
8. Hausman, Dan, and James Woodward (1999), "Independence, Invariance, and the Causal Markov Condition", British Journal for the Philosophy of Science 50: 521-583.
9. _ (2004), "Modularity and the Causal Markov Condition: A Restatement", British Journal for the Philosophy of Science 55: 147-161.
10. Hitchcock, Chris (2007), "On the Importance of Causal Taxonomy", in Alison Gopnik and Laura Schulz (eds.), Causal Learning: Psychology, Philosophy and Computation. New York: Oxford University Press, 107-116.
11. Hoover, Kevin (2003), "Nonstationary Time Series, Cointegration, and the Principle of Common Cause", British Journal of Philosophy of Science 54: 527-551.
12. Korb, Kevin, Lucas Hope, Ann Nicholson, and Karl Axnick (2004), "Varieties of Causal Intervention", in Proceedings of the Pacific Rim International Conference on AI. New York: Springer.
13. Pearl, Judea (2000). Causality. Oxford: Oxford University Press.
14. Sober, Elliott (2001), "Venetian Sea Levels, British Bread Prices, and the Principle of Common Cause", British Journal of Philosophy of Science 52: 331-346.
15. Spirtes, Peter, Clark Glymour, and Richard Scheines (2000), Causation, Prediction and Search. Springer Lecture Notes in Statistics. Cambridge, MA: MIT Press.
16. Steel, Dan (2005), "Indeterminism and the Causal Markov Condition", British Journal of Philosophy of Science 56: 3-26.
17. Woodward, James (2003), Making Things Happen. Oxford: Oxford University Press.