Causality, probability, and time

Causality, Probability, and Time

Volumn 9781107026483, Issue , 2009, Pages 1-259

  Kleinberg, Samantha ^a  

^a STEVENS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CASE-STUDIES; COMPUTATIONAL RESEARCHES; ELECTRONIC HEALTH RECORD; KEY PARTS; NEW APPROACHES; PRACTICAL USE; STOCK MARKET CRASHES;

COMPLEX NETWORKS;

EID: 84923615464     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9781139207799     Document Type: Book

References (184)

1. Agbabiaka, T. B., Savovic, J., and Ernst, E. (2008). Methods for Causality Assessment of Adverse Drug Reactions: A Systematic Review. Drug Safety, 31(1):21-37.
2. Alicke, M.D. (1992).Culpable Causation. Journal of Personality and Social Psychology, 63(3):368-378.
3. Anscombe, G. E. M. (1971). Causality and Determination: An Inaugural Lecture. Cambridge University Press, London. Reprinted in Sosa and Tooley, 1993.
4. Benjamini, Y. and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1):289-300.
5. Benjamini, Y. and Yekutieli, D. (2001). The Control of the False Discovery Rate in Multiple Testing under Dependency. Annals of Statistics, 29(4):1165-1188.
6. Bickel, P. J., Hammel, E. A., and O’Connell, J.W. (1975). Sex Bias in Graduate Admissions: Data from Berkeley. Science, 187(4175):398-404.
7. Blinowska, K. J., Kus, R., and Kaminski, M. (2004). Granger causality and information flow in multivariate processes. Physics Review E, 70(5):050902.
8. Brandt, P. (2009). MSBVAR R package version 0.4.
9. Bressler, S. L. and Seth, A. K. (2011). Wiener-Granger Causality: A well established methodology. Neuroimage, 58(2):323-329.
10. Broadbent, A. (2012). Causes of causes. Philosophical Studies, 158(3):457-476.
11. Brown, E. N., Kass, R. E., and Mitra, P. P. (2004). Multiple neural spike train data analysis: state-of-the-art and future challenges. Nature Neuroscience, 7(5):456-461.
12. Cartwright, N. (1979). Causal Laws and Effective Strategies. Noˆus, 13(4):419-437.
13. Cartwright, N. (1989). Nature’s Capacities and Their Measurement. Clarendon Press, Oxford.
14. Cartwright, N. (2001). What Is Wrong with Bayes Nets? The Monist, 84(2):242-264.
15. Cartwright, N. (2002). 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(3):411-453.
16. Cartwright, N. (2007a). Causal Powers: What Are They? Why Do We Need Them? What Can Be Done with Them and What Cannot? Technical report, Centre for Philosophy of Natural and Social Science, London School of Economics and Political Science, London.
17. Cartwright, N. (2007b). Hunting Causes and Using Them: Approaches in Philosophy and Economics. Cambridge University Press, Cambridge.
18. Chan, K., Poernomo, I., Schmidt, H., and Jayaputera, J. (2005). A Model-Oriented Framework for Runtime Monitoring of Nonfunctional Properties. In Quality of Software Architectures and Software Quality, volume 3712 of Lecture Notes in Computer Science, pp. 38-52. Springer, New York.
19. Chowdhury, A. R. (1987). Are causal relationships sensitive to causality tests? Applied Economics, 19(4):459-465.
20. Ciesinski, F. and Großer, M. (2004). On Probabilistic Computation Tree Logic. In Validation of Stochastic Systems, volume 2925 of Lecture Notes in Computer Science, pp. 333-355. Springer, New York.
21. Clarke, E. M., Emerson, E. A., and Sistla, A. P. (1986). Automatic Verification of Finite-State Concurrent Systems Using Temporal Logic Specifications. ACM Transactions on Programming Languages and Systems, 8(2):244-263.
22. Clarke, E. M., Grumberg, O., and Peled, D. A. (1999). Model Checking. The MIT Press, Cambridge, MA.
23. Cooper, G. F. (1999). An Overview of the Representation and Discovery of Causal Relationships Using Bayesian Networks. In C. Glymour and G. F. Cooper (eds.), Computation, Causation, and Discovery, pp. 3-62. AAAI Press/The MIT Press, Cambridge, MA.
24. Crick, F. (1970). Central Dogma of Molecular Biology. Nature, 227(5258):561-563.
25. Davidson, D. (1998). The Individuation of Events. In C. Macdonald and S. Laurence (eds.), Contemporary Readings in the Foundations of Metaphysics, pp. 295-309. Wiley-Blackwell, Oxford.
26. Davis, R. (1989). Expert Systems: How Far Can They Go? AI Magazine, 10(1):61-67.
27. Dekkers, O. M., Elm, E., Algra, A., Romijn, J. A., and Vandenbroucke, J. P. (2010). How to assess the external validity of therapeutic trials: a conceptual approach. International Journal of Epidemiology, 39(1):89-94.
28. Ding, M., Chen, Y., and Bressler, S. L. (2006). Granger Causality: Basic Theory and Application to Neuroscience. In B. Schelter, M. Winterhalder, and J. Timmer (eds.), Handbook of Time Series Analysis: Recent Theoretical Developments and Applications, pp. 437-460. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
29. Dondelinger, F., Lebre, S., and Husmeier, D. (2010). Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing. In Proceedings of the 27th International Conference on Machine Learning.
30. Dowe, P. (2000). Physical Causation. Cambridge University Press, Cambridge.
31. Dupre, J. (1984). Probabilistic Causality Emancipated. Midwest Studies in Philosophy, 9(1):169-175.
32. Dupre, J. (1990). Probabilistic Causality: A Rejoinder to Ellery Eells. Philosophy of Science, 57(4):690-698.
33. Edgington, D. (1995). On Conditionals. Mind, 104(414):235-329.
34. Eells, E. (1987a). Probabilistic Causality: Reply to John Dupr´e. Philosophy of Science, 54(1):105-114.
35. Eells, E. (1987b). Cartwright and Otte on Simpson’s Paradox. Philosophy of Science, 54(2):233-243.
36. Eells, E. (1991). Probabilistic Causality. Cambridge University Press, Cambridge.
37. Eells, E. and Sober, E. (1983). Probabilistic Causality and the Question of Transitivity. Philosophy of Science, 50(1):35-57.
38. Efron, B. (2004). Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis. Journal of the American Statistical Association, 99(465):96-104.
39. Efron, B. (2007). Size, Power, and False Discovery Rates. The Annals of Statistics, 35(4):1351-1377.
40. Efron, B. (2010). Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Institute of Mathematical Statistics Monographs. Cambridge University Press, Cambridge.
41. Efron, B. and Tibshirani, R. (2002). Empirical Bayes Methods and False Discovery Rates for Microarrays. Genetic Epidemiology, 23(1):70-86.
42. Eichler, M. (2009). Causal inference from multivariate time series: What can be learned from Granger causality. In C. Glymour, W. Wang, and D. Westerstahl (eds.), Logic, Methodology and Philosophy of Science: Proceedings of the 13th International Congress, pp. 481-496. College Publications, London.
43. Entner, D. and Hoyer, P. O. (2010). On Causal Discovery from Time Series Data using FCI. In Proceedings of the 5th European Workshop on Probabilistic Graphical Models.
44. Fagin, R., Halpern, J. Y., Moses, Y., and Vardi, M. Y. (1995). Reasoning About Knowledge. The MIT Press, Cambridge, MA.
45. Fama, E. F. and French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2):427-465.
46. Fama, E. F. and French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1):3-56.
47. Fienberg, S. E. (1980). The Analysis of Cross-Classified Categorical Data. The MIT Press, Cambridge, MA, 2nd edition. Reprint, Springer-Verlag, 2007.
48. Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh.
49. Fitelson, B. and Hitchcock, C. R. (2011). Probabilistic Measures of Causal Strength. In P. M. Illari, F. Russo, and J.Williamson (eds.), Causality in the Sciences, pp. 600-627. Oxford University Press, Oxford.
50. Freedman, D. and Humphreys, P. (1999). Are There Algorithms That Discover Causal Structure? Synthese, 121(1-2):29-54.
51. French, K. R. and Fama, E. F. (2011). Fama French - Data Library. http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data library.html.
52. Friedman, N., Murphy, K., and Russell, S. (1998). Learning the Structure of Dynamic Probabilistic Networks. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence.
53. Gillies, D. (2000). Philosophical Theories of Probability. Routledge, London.
54. Glennan, S. (1996). Mechanisms and the Nature of Causation. Erkenntnis, 44(1):49-71.
55. Glennan, S. (2002). Rethinking Mechanistic Explanation. Philosophy of Science, 69(3): S342-S353.
56. Glymour, C., Spirtes, P., and Richardson, T. (1999). On the Possibility of Inferring Causation from Association without Background Knowledge. In C. Glymour and G. F. Cooper (eds.), Computation, Causation, and Discovery, pp. 305-322. AAAI Press/The MIT Press, Cambridge, MA.
57. Glymour, C., Scheines, R., Spirtes, P., and Ramsey, J. (2004). TETRAD IV software.
58. Godfrey-Smith, P. (2010). Causal Pluralism. In H. Beebee, C. R. Hitchcock, and P. Menzies (eds.), Oxford Handbook of Causation, pp. 326-337. Oxford University Press, Oxford.
59. Good, I. J. (1961a). A Causal Calculus (I). British Journal for the Philosophy of Science, 11(44):305-318.
60. Good, I. J. (1961b). A Causal Calculus (II). British Journal for the Philosophy of Science, 12(45):43-51.
61. Gopnik, A. (2004). Finding Our Inner Scientist. Daedalus, 133(1):21-28.
62. Gopnik, A. and Schulz, L. (eds.) (2007). Causal Learning: Psychology, Philosophy, and Computation. Oxford University Press, Oxford.
63. Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, 37(3):424-438.
64. Granger, C. W. J. (1980). Testing for Causality: A Personal Viewpoint. Journal of Economic Dynamics and Control, 2:329-352.
65. Granger, C. W. J. (2007). Causality in Economics. In P. K. Machamer and G. Wolters (eds.), Thinking about Causes: From Greek Philosophy to Modern Physics, pp. 284-296. University of Pittsburgh Press, Pittsburgh.
66. Grzegorczyk, M. and Husmeier, D. (2009). Non-stationary continuous dynamic Bayesian networks. In Proceedings of the 23rd Annual Conference on Neural Information Processing Systems.
67. Halpern, J. Y. and Hitchcock, C. R. (2010). Actual Causation and the Art of Modeling. In R. Dechter, H. Geffner, and J.Y. Halpern (eds.), Heuristics, Probability and Causality: A Tribute to Judea Pearl, pp. 383-406. College Publications, London.
68. Halpern, J. Y. and Pearl, J. (2005). Causes and Explanations: A Structural-Model Approach. Part I: Causes. British Journal for the Philosophy of Science, 56(4):843-887.
69. Halpern, J. Y. and Shoham, Y. (1991). A Propositional Modal Logic of Time Intervals. Journal of the ACM, 38(4):935-962.
70. Hansson, H. and Jonsson, B. (1994). A Logic for Reasoning about Time and Reliability. Formal Aspects of Computing, 6(5):512-535.
71. Hartemink, A. J. (2008). Banjo: Bayesian Network Inference with Java Objects. http://www.cs.duke.edu/amink/software/banjo/.
72. Hausman, D. M. (1998). Causal Asymmetries. Cambridge University Press, Cambridge.
73. Hausman, D. M. (2005). Causal Relata: Tokens, Types, or Variables? Erkenntnis, 63(1):33-54.
74. Hausman, D. M. and Woodward, J. (1999). Independence, Invariance and the Causal Markov Condition. British Journal for the Philosophy of Science, 50(4):521-583.
75. Hausman, D. M. and Woodward, J. (2004). Modularity and the Causal Markov Condition: A Restatement. British Journal for the Philosophy of Science, 55(1):147-161.
76. Hesse, W., Moller, E., Arnold, M., and Schack, B. (2003). The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies. Journal of Neuroscience Methods, 124(1):27-44.
77. Hill, A. B. (1965). The Environment and Disease: Association or Causation? Proceedings of the Royal Society of Medicine, 58(5):295-300.
78. Hintikka, J. (1962). Knowledge and Belief: An Introduction to the Logic of the Two Notions. Cornell University Press, Ithaca.
79. Hitchcock, C. R. (1995). The Mishap at Reichenbach Fall: Singular vs. General Causation. Philosophical Studies, 78(3):257-291.
80. Hitchcock, C. R. (2003). Of Humean Bondage. British Journal for the Philosophy of Science, 54(1):1-25.
81. Hitchcock, C. R. (2007). How to be a Causal Pluralist. In P. K. Machamer and G. Wolters (eds.), Thinking about Causes: From Greek Philosophy to Modern Physics, pp. 200-221. University of Pittsburgh Press, Pittsburgh.
82. Hlavackova-Schindler, K., Palus, M., Vejmelka, M., and Bhattacharya, J. (2007). Causality detection based on information-theoretic approaches in time series analysis. Physics Reports, 441(1):1-46.
83. Hoover, K. D. (2001). Causality in Macroeconomics. Cambridge University Press, Cambridge.
84. Hopkins, M. and Pearl, J. (2007). Causality and Counterfactuals in the Situation Calculus. Journal of Logic and Computation, 17(5):939-953.
85. Hripcsak, G., Elhadad, N., Chen, Y. H., Zhou, L., and Morrison, F. P. (2009). Using Empiric Semantic Correlation to Interpret Temporal Assertions in Clinical Texts. Journal of the American Medical Informatics Association, 16(2):220-227.
86. Hume, D. (1739). A Treatise of Human Nature. London. Reprint, Prometheus Books, 1992. Citations refer to the Prometheus edition.
87. Hume, D. (1748). An Enquiry Concerning Human Understanding. London. Reprint, Dover Publications, 2004.
88. Humphreys, P. and Freedman, D. (1996). The Grand Leap. British Journal for the Philosophy of Science, 47(1):113-123.
89. Hyttinen, A., Eberhardt, F., and Hoyer, P. O. (2010). Causal Discovery for Linear Cyclic Models with Latent Variables. In Proceedings of the 5th European Workshop on Probabilistic Graphical Models.
90. Jacobs, R. L., Leamer, E. E., and Ward, M. P. (1979). Difficulties with Testing for Causation. Economic Inquiry, 17(3):401-413.
91. Jaynes, E. T. (2003). Probability Theory: The Logic of Science. Cambridge University Press, Cambridge.
92. Jennings, D. L., Amabile, T. M., and Ross, L. (1982). Informal covariation assessment: Data-based versus theory-based judgments. In D. Kahneman, P. Slovic, and A. Tversky (eds.), Judgment Under Uncertainty: Heuristics and Biases, pp. 211-230. Cambridge University Press, Cambridge.
93. Jin, J. and Cai, T. T. (2007). Estimating the Null and the Proportion of Nonnull Effects in Large-scale Multiple Comparisons. Journal of the American Statistical Association, 102(478):495-506.
94. Joffe, M. (2011). The gap between evidence discovery and actual causal relationships. Preventive Medicine, 53(4-5):246-249.
95. Johnson, M. L., Crown, W., Martin, B. C., Dormuth, C. R., and Siebert, U. (2009). Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report-Part III. Value in Health, 12(8):1062-1073.
96. Kaminski, M., Ding, M., Truccolo, W. A., and Bressler, S. L. (2001). Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biological Cybernetics, 85(2):145-157.
97. Kim, J. (1976). Events as Property Exemplifications. In M. Brand and D. Walton (eds.), Action Theory, pp. 159-177. Reidel, Dordrecht. Reprinted in Laurence and Macdonald, 1998.
98. Kim, M., Kannan, S., Lee, I., Sokolsky, O., and Viswanathan, M. (2001). Java-MaC: A Run-time Assurance Tool for Java Programs. Electronic Notes in Theoretical Computer Science, 55(2):218-235.
99. Kleinberg, S. (2011). A Logic for Causal Inference in Time Series with Discrete and Continuous Variables. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence.
100. Kleinberg, S. and Hripcsak, G. (2011). A review of causal inference for biomedical informatics. Journal of Biomedical Informatics, 44(6):1102-1112.
101. Kleinberg, S. and Mishra, B. (2009). The Temporal Logic of Causal Structures. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence.
102. Kleinberg, S. and Mishra, B. (2010). The Temporal Logic of Token Causes. In Proceedings of the 12th International Conference on the Principles of Knowledge Representation and Reasoning.
103. Kleinberg, S., Kolm, P., and Mishra, B. (2010). Investigating Causal Relationships in Stock Returns with Temporal Logic Based Methods. arXiv e-prints/1006.1791.
104. Knobe, J. (2009). Folk judgments of causation. Studies In History and Philosophy of Science, 40(2):238-242.
105. Koch, R. (1932). Die Aetiologie der Tuberkulose. Journal of Molecular Medicine, 11(12):490-492.
106. Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. The MIT Press, Cambridge, MA.
107. Kolmogorov, A.N. (1956). Foundations of the Theory of Probability. Chelsea Publishing Co., New York.
108. Korb, K. B. and Nicholson, A. E. (2010). Bayesian Artificial Intelligence. CRC press, Boca Raton, 2nd edition.
109. Kripke, S. (1963). Semantical Considerations on Modal Logic. Acta Philosophica Fennica, 16(1963):83-94.
110. Lagnado, D. (2011). Thinking about Evidence. In P. Dawid, W. Twining, and M. Vasilaki (eds.), Evidence, Inference and Enquiry, pp. 183-224. Oxford University Press/British Academy, Oxford.
111. Laurence, S. and Macdonald, C. (eds.) (1998). Contemporary Readings in the Foundations of Metaphysics. Wiley-Blackwell, Oxford.
112. Lauritzen, S. L. and Wermuth, N. (1989). Graphical Models for Associations Between Variables, some of which are Qualitative and some Quantitative. The Annals of Statistics, 17(1):31-57.
113. Leibovici, L. (2001). Effects of remote, retroactive intercessory prayer on outcomes in patients with bloodstream infection: randomised controlled trial. BMJ, 323(7327):1450-1451.
114. Leucker, M. and Schallhart, C. (2009). A Brief Account of Runtime Verification. Journal of Logic and Algebraic Programming, 78(5):293-303.
115. Lewis, D. (1973). Causation. The Journal of Philosophy, 70(17):556-567. Reprinted in Lewis, 1986a.
116. Lewis, D. (1986a). Philosophical Papers, volume 2. Oxford University Press, Oxford.
117. Lewis, D. (1986b). Postscripts to “Causation”. In Philosophical Papers, volume 2, pp. 172-213. Oxford University Press, Oxford.
118. Lewis, D. (2000). Causation as Influence. The Journal of Philosophy, 97(4):182-197.
119. Lunze, J. and Schiller, F. (1999). An Example of Fault Diagnosis by Means of Probabilistic Logic Reasoning. Control Engineering Practice, 7(2):271-278.
120. Machamer, P., Darden, L., and Craver, C. F. (2000). Thinking about Mechanisms. Philosophy of Science, 67(1):1-25.
121. Mackie, J. L. (1965). Causes and Conditions. American Philosophical Quarterly, 2(4):245-264.
122. Mackie, J. L. (1974). The Cement of the Universe. Clarendon Press, Oxford.
123. McDermott, M. (1995). Redundant Causation. British Journal for the Philosophy of Science, 46(4):523-544.
124. McIntosh, N. (2002). Intensive care monitoring: past, present and future. Clinical Medicine, 2(4):349-355.
125. Mill, J. S. (1843). A System of Logic. London. Reprint, Lincoln-Rembrandt Pub., 1986.
126. Moor, J. H. (ed.) (2003). The Turing Test: The Elusive Standard of Artificial Intelligence. Kluwer Academic Publishers, Dordrecht.
127. Morris, A. and Gardner, R. (1992). Computer applications. In J. Hall, G. Schmidt, and L. Wood (eds.), Principles of Critical Care, pp. 500-514. McGraw-Hill, New York.
128. Murphy, K. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, University of California, Berkley.
129. Naranjo, C. A., Busto, U., Sellers, E. M., Sandor, P., Ruiz, I., Roberts, E. A., Janecek, E., Domecq, C., and Greenblatt, D. J. (1981). A method for estimating the probability of adverse drug reactions. Clinical Pharmacology and Therapeutics, 30(2):239-245.
130. Neapolitan, R. (2004). Learning Bayesian Networks. Pearson Prentice Hall, Upper Saddle River, NJ.
131. Otte, R. (1981). A Critique of Suppes’ Theory of Probabilistic Causality. Synthese, 48(2):167-189.
132. Otte, R. (1985). Probabilistic Causality and Simpson’s Paradox. Philosophy of Science, 52(1):110-125.
133. Papineau, D. (1992). Can We Reduce Causal Direction to Probabilities? In PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, volume 2, pp. 238-252.
134. Papineau, D. (2001). Metaphysics over Methodology - Or, Why Infidelity Provides No Grounds To Divorce Causes from Probabilities. In M. C. Galavotti, P. Suppes, and D. Costantini (eds.), Stochastic Causality, pp. 15-38. CSLI, Stanford, CA.
135. Parascandola, M. and Weed, D. L. (2001). Causation in epidemiology. Journal of Epidemiology and Community Health, 55(12):905-912.
136. Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge.
137. Pnueli, A. (1977). The Temporal Logic of Programs. In Proceedings of the 18th Annual Symposium on Foundations of Computer Science.
138. Prior, A. N. (1967). Past, Present, and Future. Clarendon Press, Oxford.
139. Psillos, S. (2010). Causal Pluralism. In R. Vanderbeeken and B. D’Hooghe (eds.), Worldviews, Science and Us: Studies of Analytical Metaphysics, pp. 131-151. World Scientific Publishers, Singapore.
140. Reichenbach, H. (1956). The Direction of Time. University of California Press, Berkeley. Reprint, Dover Publications, 2000.
141. Reiss, J. (2007). Time Series, Nonsense Correlations and the Principle of the Common Cause. In F. Russo and J.Williamson (eds.), Causality and Probability in the Sciences, pp. 179-196. College Publications, London.
142. Reiter, R. (1980). A Logic for Default Reasoning. Artificial Intelligence, 13(1-2):81-132.
143. Richardson, T. (1996). A Discovery Algorithm for Directed Cyclic Graphs. In Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence.
144. Rizzi, D. A. (1994). Causal Reasoning and the Diagnostic Process. Theoretical Medicine and Bioethics, 15(3):315-333.
145. Robins, J. M. and Wasserman, L. (1999). On the Impossibility of Inferring Causation from Association without Background Knowledge. In C. Glymour and G. Cooper (eds.), Computation, Causation, and Discovery, pp. 305-321. AAAI Press/The MIT Press, Cambridge, MA.
146. Robinson, J. W. and Hartemink, A. J. (2010). Learning Non-Stationary Dynamic Bayesian Networks. Journal of Machine Learning Research, 11:3647-3680.
147. Rosen, D. A. (1978). In Defense of a Probabilistic Theory of Causality. Philosophy of Science, 45(4):604-613.
148. Rothman, K. J. (1976). Causes. American Journal of Epidemiology, 104(6):587-592. Reprinted in 141(2), 1995.
149. Rothwell, P. M. (2006). Factors That Can Affect the External Validity of Randomised Controlled Trials. PLoS Clinical Trials, 1(1):e9.
150. Russo, F. (2006). The Rationale ofVariation in Methodological and Evidential Pluralism. Philosophica, 77(1):97-124.
151. Russo, F. (2009). Causality and Causal Modelling in the Social Sciences: Measuring Variations. Springer, New York.
152. Russo, F. and Williamson, J. (2007). Interpreting Causality in the Health Sciences. International Studies in the Philosophy of Science, 21(2):157-170.
153. Russo, F. and Williamson, J. (2011a). Epistemic Causality and Evidence-Based Medicine. History and Philosophy of the Life Sciences, 33(4):563-581.
154. Russo, F. and Williamson, J. (2011b). Generic versus single-case causality: the case of autopsy. European Journal for Philosophy of Science, 1(1):47-69.
155. Salmon, W. C. (1980a). Causality: Production and Propagation. In PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, volume 2, pp. 49-69.
156. Salmon, W. C. (1980b). Probabilistic Causality. Pacific Philosophical Quarterly, 61:50-74.
157. Salmon, W. C. (1994). Causality without Counterfactuals. Philosophy of Science, 61(2):297-312.
158. Schmidt, M. and Murphy, K. (2009). Modeling Discrete Interventional Data using Directed Cyclic Graphical Models. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence.
159. Schulz, K. F., Chalmers, I., Hayes, R. J., and Altman, D. G. (1995). Empirical Evidence of Bias: Dimensions of Methodological Quality Associated with Estimates of Treatment Effects in Controlled Trials. JAMA, 273(5):408-412.
160. Seth, A. K. (2010). A MATLAB toolbox for Granger causal connectivity analysis. Journal of Neuroscience Methods, 186(2):262-273.
161. Shoham, Y. (1988). Reasoning About Change: Time and Causation from the Standpoint of Artificial Intelligence. The MIT Press, Cambridge, MA.
162. Simpson, E. H. (1951). The Interpretation of Interaction in Contingency Tables. Journal of the Royal Statistical Society. Series B (Methodological), 13(2):238-241.
163. Skyrms, B. (1980). Causal Necessity. Yale University Press, New Haven.
164. Skyrms, B. (1984). EPR: Lessons for Metaphysics. Midwest Studies in Philosophy, 9(1):245-255.
165. Sloman, S. A. (2005). Causal Models: How People Think about the World and its Alternatives. Oxford University Press, Oxford.
166. Sloman, S. A., Fernbach, P. M., and Ewing, S. (2009). Causal Models: The Representational Infrastructure for Moral Judgment. In D. M. Bartels, C.W. Bauman, L. J. Skitka, and D. L. Medin (eds.), Moral Judgment and Decision Making: The Psychology of Learning and Motivation, vol. 50, pp. 1-26. Elsevier, San Diego, CA.
167. Sober, E. (2001). Venetian Sea Levels, British Bread Prices, and the Principle of the Common Cause. British Journal for the Philosophy of Science, 52(2):331-346.
168. Sober, E. and Papineau, D. (1986).Causal Factors, Causal Inference, Causal Explanation. Proceedings of the Aristotelian Society, Supplementary Volumes, 60:97-136.
169. Sosa, E. and Tooley, M. (eds.) (1993). Causation. Oxford University Press, Oxford.
170. Spirtes, P. (1995). Directed Cyclic Graphical Representations of Feedback Models. In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence.
171. Spirtes, P., Glymour, C., and Scheines, R. (2000). Causation, Prediction, and Search. The MIT Press, Cambridge, MA, 2nd edition. First published 1993.
172. Stalnaker, R. (1968). A Theory of Conditionals. In N. Rescher (ed.), Studies in Logical Theory, American Philosophy Quarterly Monograph Series, pp. 98-112. Blackwell, Oxford.
173. Storey, J. D. (2003). The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31(6):2013-2035.
174. Storey, J. D. and Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences, 100(16):9440-9445.
175. Strimmer, K. (2008). fdrtool: a versatile R package for estimating local and tail areabased false discovery rates. Bioinformatics, 24(12):1461-1462.
176. Suppes, P. (1970). A Probabilistic Theory of Causality. North-Holland, Amsterdam.
177. Susser, M. (1973). Causal Thinking in the Health Sciences: Concepts and Strategies of Epidemiology. Oxford University Press, New York.
178. Thurman, W. N. and Fisher, M. E. (1988). Chickens, Eggs, and Causality, or Which Came First. American Journal of Agricultural Economics, 70(2):237-238.
179. Wiener, N. (1956). The theory of prediction. In E. Beckenbach (ed.), Modern Mathematics for the Engineer, pp. 165-190. McGraw-Hill, New York.
180. Wikipedia (2008). Ronald Opus - Wikipedia, The Free Encyclopedia. http://en.wikipedia.org/w/index.php?title=RonaldOpus&oldid=247157374.
181. Williamson, J. (2006). Causal Pluralism versus Epistemic Causality. Philosophica, 77(1):69-96.
182. Woodward, J. (2005). Making Things Happen: A Theory of Causal Explanation. Oxford University Press, New York.
183. Yoon, Y., Guimaraes, T., and O’Neal, Q. (1995). Exploring The Factors Associated With Expert Systems Success. MIS quarterly, 19(1):83-106.
184. Zou, C. and Feng, J. (2009). Granger causality vs. dynamic Bayesian network inference: a comparative study. BMC Bioinformatics, 10:122.