Causality References

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2. Cartwright, N. Against modularity, the causal Markov condition, and any link between the two: Comments on Hausman and Woodward. British Journal for the Philosophy of Science, 53. 411-453.

3. Cartwright, N. From metaphysics to method: Comments on manipulability and the causal markov condition. British Journal for the Philosophy of Science, 57. 197-218.

4. Cooper, G.F. An overview of the representation and discovery of causal relationships using Bayesian networks. in Glymour, C. and Cooper, G.F. eds. Computation, Causation, and Discovery, AAAI Press and MIT Press, 1999, 3-62.

5. Cooper, G.F. and Yoo, C. Causal discovery from a mixture of experimental and observational data. in Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, 1999, 116-125.

6. Dahlhaus, R. and Eichler, M. Causality and Graphical models in Time series. in Green, P., Hjort, N. and Richardson, S. eds. Highly structured stochastic systems., University Press, Oxford, 2003.

7. Dash, D.H. and Druzdzel, M.J., A Hybrid Anytime Algorithm for the Construction of Causal Models From Sparse Data. in Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI-99), (Stockholm, Sweden, 1999), Morgan Kaufmann Publishers, San Francisco, CA, 142-149.

8. Dean, T. and Kanazawa, K. A model for reasoning about persistence and causation. Computational Intelligence, 5 (3). 142-150.

9. Druzdzel, M.J., Lu, T.C. and Leong, T.-Y., Interactive construction of decision models based on causal mechanisms. in Working Notes of the AAAI Spring Symposium on Interactive and Mixed-initiative Decision-Theoretic Systems, (1998).

10. Eberhardt, F., Glymour, C. and Scheines, R. N-1 Experiments Suffice to Determine the Causal Relations Among N Variables, In Department of Philosophy, Carnegie Mellon University, Technical Report CMU-PHIL-161, 2004.

11. Eberhardt, F., Glymour, C. and Scheines, R. On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables. in Proceedings of the 21th Annual Conference on Uncertainty in Artificial Intelligence (UAI-05), AUAI Press, 2005, 178-184.

12. Eichle, M. Granger Causality and path diagrams for multivariate time series. Journal of Econometrics, 137 (2). 334-353.

13. Gammerman, A. (ed.), Causal models and intelligent data management. Springer, Berlin, 1999.

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15. Geweke, J., Meese, R. and Dent, W. Comparing Alternative Tests of Causaltiy in Temporal Systems. Journal of Econometrics., 21. 161-194.

16. Glymour, C. and Cooper, G.F. (eds.). Computation, Causation, and Discovery. MIT Press, Cambridge, MA, USA, 1999.

17. Granger, C.W.J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica., 37 (3). 424-438.

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19. Griffiths, T.L., Baraff, E.R. and Tenenbaum, J.B., Using physical theories to infer hidden causal structure. in Proceedings of the 26th Annual Conference of the Cognitive Science Society, (2004).

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21. Heckerman, D. A Bayesian Approach to Learning Causal Networks. in Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, San Francisco, CA, 1995, 285-295.

22. Heckerman, D., Meek, C. and Cooper, G.F. A Bayesian approach to causal discovery. in Glymour, C. and Cooper, G.F. eds. Computation, Causation, Discovery, MIT Press, Cambridge, MA, USA, 1999, 141-165.

23. Heckerman, D. and Shachter, R. Decision-Theoretic Foundations for Causal Reasoning. Journal of Artificial Intelligence Research, 3. 405-430.

24. Holland, P. Statistics and causal inference. Journal of the American Statistical Association, 81. 945-960.

25. Iwasaki, Y. and Simon, H.A. Causality in device behavior. Artificial Intelligence, 29 (1). 3-32.

26. Kim, J.H. and Pearl, J. A Computational Model for Causal and Diagnostic Reasoning in Inference Systems. in Proceedings of the 8th International Joint Conference on Artificial Intelligence (IJCAI), 1983, 190-193.

27. Korb, K.B. and Wallace, C.S. In search of the philosopher's stone: Remarks on Humphreys and Freedman's critique of causal discovery. British Journal for the Philosophy of Science, 48. 543-553.

28. Lagnado, D.A. and Sloman, S.A., Learning causal structure. in Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society, (Maryland, 2002).

29. Lauritzen, S.L. Causal inference from graphical models. in Barndorff-Nielesen, E., Cox, D.R. and Kluppelberg, C. eds. Complex stochastic systems, CRC Press, London, 2001.

30. Li, G. and Leong, T.-Y., Learning Causal Bayesian Network with Constraints from Domain Knowledge. manuscript. in, (2005).

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32. McKim, V.R. and Turner, S.P. (eds.). Causality in crisis? statistical methods and the search for causal knowledge in the social sciences. University of Notre Dame Press, Notre Dame, 1997.

33. Meganck, S., Leray, P. and Manderick, B. Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach. in Proceedings of Modelling Decisions in Artificial Intelligence (MDAI 2006), LNAI 3885, 2006, 58-69.

34. Moneta, A. Causality and econometrics: some philosophical underpinnings, 2004.

35. Morjaria, M. and Santosa, F. Monitoring Complex Systems with Causal Networks. IEEE Computational Science and Engineering, 3 (4). 9-10.

36. Murphy, K. Active Learning of Causal Bayes Net Structure. Technical report, Computer Science Division, University of California, Berkeley, CA, 2001.

37. Nadkarni, S. and Shenoy, P.P. A Causal Mapping Approach to Constructing Bayesian Networks. Decision Support Systems, 38 (2). 259-281.

38. Neal, R.M. On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note). Journal of Artificial Intelligence Research, 12. 87-91.

39. O'Donnell, R.T., Nicholson, A.E., Han, B., Korb, K.B., Alam, M.J. and Hope, L.R. Incorporating Expert Elicited Structural Information in the CaMML Causal Discovery Program. Technical report 2006/194, Clayton School of Information Technology, Monash University, Melbourne, 2006.

40. Pearl, J. Bayesian Networks, Causal Inference and Knowledge Discovery, Technical Report (R-281), UCLA Cognitive Systems Laboratory, 2001.

41. Pearl, J. Causal diagrams for empirical research. Biometrika, 82 (4). 669-688.

42. Pearl, J. Causality: models, reasoning, and inference. Cambridge University Press, New York, 2000.

43. Pearl, J. and Verma, T., A theory of inferred causation. in Principles of Knowledge Representation and Reasoning: Proceeding of the Second International Conference, (San Mateo, CA, 1991), Morgan Kaufmann, 441-452.

44. Price, H. Agency and causal asymmetry. Mind, 101 (403). 501-520.

45. Price, J.M. A Characterization of instantaneous Causaltiy: A correction. Journal of Econometrics, 10. 253-256.

46. Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D.A. and Nolan, G.P. Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data. Science, 308 (5721). 523-529.

47. Sloman, S. Causal models: how people think about the world and its alternatives. Oxford University Press, New York, 2005.

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49. Spirtes, P., Glymour, C. and Scheines, R. Causation, Prediction, and Search (Second Edition). MIT Press, Cambridge, MA, USA, 2000.

50. Spirtes, P., Glymour, C. and Scheines, R. Causation, Prediction, and Search. Number 81 in Lecture Notes in Statistics. Springer Verlag, New York, 1993.

51. Spirtes, P., Glymour, C. and Scheines, R. From probability to causality. Philosophical Studies, 64 (1). 1-36.

52. Suppes, P. A Probabilistic Theory of Causality. North Holland, Amsterdam, 1970.

53. Tian, J. and Pearl, J. Causal Discovery from Changes. in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), Morgan Kaufmann, San Francisco, CA, 2001, 512-521.

54. Tsamardinos, I., Aliferis, C.F. and Statnikov, A., Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations. in Proceedings of The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003), (2003), 673-678.

55. Verma, T. and Pearl, J., Equivalence and synthesis of causal models. in Proceedings of Sixth Conference on Uncertainty in Artificial Intelligence, (Boston, MA, 1990), Morgan Kaufmann, 220-227.

56. Wallace, C.S. and Korb, K.B. Learning Linear Causal Models by MML Sampling. in Gammerman, A. ed. Causal models and intelligent data management, Springer, 1999, 89-111.

57. Wallace, C.S., Korb, K.B. and Dai, H., Causal discovery via MML. in Proceedings of the Thirteenth International Conference of Machine Learning (ICML'96), (San Francisco CA USA, 1996), Morgan Kaufmann, 516-524.

58. Williamson, J. Learning causal relationships, Discussion Paper 02/02, LSE Centre for Natural and Social Sciences.

59. Woodward, J. Making things happen: a theory of causal explanation. Oxford University Press, 2003.

60. Yoo, C. and Cooper, G.F. Causal Discovery of Latent Variable Models from a Mixture of Experimental and Observational Data. CBMI Research Report CBMI-173, 2001.

61. Yoo, C., Thorsson, V. and Cooper, G.F., Discovery of Causal Relationships in a Gene-regulation Pathway from a Mixture of Experimental and Observational DNA Microarray Data. in Proceedings of Pacific Symposium on Biocomputing, (2002), World Scientific, 498-509.

62. Zellner, A., Causality and econometrics. in Three Aspects of Policy and Policymaking: Knowledge, Data and Institutions, Carnegie-Rochester Conference Series on Public Policy, (1979), 9-54.

63. Zhang, N.L. and Poole, D. Exploiting Causal Independence in Bayesian Network Inference. Journal of Artificial Intelligence Research, 5. 301-328.