A review of causal inference for biomedical informatics

Journal of Biomedical Informatics

Volumn 44, Issue 6, 2011, Pages 1102-1112

  Kleinberg, Samantha ^a   Hripcsak, George ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Causal explanation; Causal inference; Electronic health records

Indexed keywords

BIOMEDICAL INFORMATICS; CAUSAL EXPLANATION; CAUSAL INFERENCE; ELECTRONIC HEALTH RECORD; GRANGER CAUSALITY; GRAPHICAL MODEL; HEALTH SCIENCE; INFORMATICS; OBSERVATIONAL DATA; RISK FACTORS;

HEALTH; INFORMATION SCIENCE; PHILOSOPHICAL ASPECTS; RECORDS MANAGEMENT; TEMPORAL LOGIC;

HEALTH RISKS;

ALGORITHM; BAYES THEOREM; BIOINFORMATICS; BIOMEDICAL INFORMATICS; CAUSAL ATTRIBUTION; CAUSAL MODELING; COMPUTER ANALYSIS; COMPUTER SYSTEM; HUMAN; LOGIC; LUNG CANCER; MEDICAL INFORMATICS; PERSONALIZED MEDICINE; PRIORITY JOURNAL; REVIEW; SMOKING; STRUCTURAL EQUATION MODELING; SYSTEM ANALYSIS;

BAYES THEOREM; CAUSALITY; DRUG TOXICITY; ELECTRONIC HEALTH RECORDS; EPIDEMIOLOGY; HUMANS; INDIVIDUALIZED MEDICINE; MEDICAL INFORMATICS; RISK FACTORS;

EID: 84855951317     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2011.07.001     Document Type: Review

References (114)

1. Agbabiaka T., Savovic J., Ernst E. Methods for causality assessment of adverse drug reactions: a systematic review. Drug Saf 2008, 31(1):21-37.
2. Rizzi D. Causal reasoning and the diagnostic process. Theor Med Bioeth 1994, 15(3):315-333.
3. Johnson M., Crown W., Martin B., Dormuth C., Siebert U. 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 Health 2009, 12(8):1062-1073.
4. Karhausen L. Causation: the elusive grail of epidemiology. Med Health Care Philos 2000, 3(1):59-67.
5. Parascandola M., Weed D. Causation in epidemiology. J Epidemiol Community Health 2001, 55(12):905.
6. Russo F. Variational causal claims in epidemiology. Perspect Biol Med 2009, 52(4):540-554.
7. Joffe M., Mindell J. Complex causal process diagrams for analyzing the health impacts of policy interventions. Am J Public Health 2006, 96(3):473.
8. Cochrane A. Effectiveness and efficiency: random reflections on health services 1972, Nuffield Provincial Hospitals Trust.
9. Cartwright N. Evidence-based policy: whats to be done about relevance?. Philos Stud 2009, 143(1):127-136.
10. Schulz K., Chalmers I., Hayes R., Altman D. Empirical evidence of bias: dimensions of methodological quality associated with estimates of treatment effects in controlled trials. JAMA 1995, 273(5):408.
11. Dekkers O., Elm E., Algra A., Romijn J., Vandenbroucke J. How to assess the external validity of therapeutic trials: a conceptual approach. Int J Epidemiol 2010, 39(1):89.
12. Rothwell P. Factors that can affect the external validity of randomised controlled trials. PLoS Clin Trials 2006, 1(1):e9.
13. Rothwell P. Treating individuals 1 external validity of randomised controlled trials: to whom do the results of this trial apply?. Lancet 2005, 365:82-93.
14. Victora C., Habicht J., Bryce J. Evidence-based public health: moving beyond randomized trials. Am J Public Health 2004, 94(3):400.
15. Cartwright N. Are RCTs the gold standard?. Biosocieties 2007, 2(01):11-20.
16. Cartwright N., Munro E. The limitations of randomized controlled trials in predicting effectiveness. J Eval Clin Pract 2010, 16(2):260-266.
17. Mackenzie F., Grossman J. The randomized controlled trial: gold standard, or merely standard?. Perspect Biol Med 2005, 48(4):516-534.
18. Ward A., Johnson P. Addressing confounding errors when using non-experimental, observational data to make causal claims. Synthese 2008, 163(3):419-432.
19. Granger C.W. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 1969, 37(3):424-438.
20. Pearl J. Causality: models, reasoning, and inference 2000, Cambridge University Press.
21. Rubin D. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 1974, 66(5):688-701.
22. Spirtes P., Glymour C., Scheines R. Causation, prediction, and search 2000, MIT Press.
23. Pearl J. Graphs, causality, and structural equation models. Sociol Methods Res 1998, 27(2):226.
24. Pearl J. Statistics and causal inference: a review. Test 2003, 12(2):281-345.
25. Bellazzi R., Zupan B. Predictive data mining in clinical medicine: current issues and guidelines. Int J Med Inform 2008, 77(2):81-97.
26. Wu J., Roy J., Stewart W. Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med Care 2010, 48(6):S106.
27. Suermondt H., Cooper G. An evaluation of explanations of probabilistic inference. Proceedings of the annual symposium on computer application in medical care 1992, 579. American Medical Informatics Association.
28. Reichenbach H. The direction of time 1956, University of California Press, Berkeley, CA.
29. Lewis D. Causal explanation. Philos Pap 1986, 2:214-240.
30. Aristotle. Physics, vol. 2. The Internet Classics Archive; 1994.
31. Hume D. An enquiry concerning human understanding; 1748.
32. Mill J.S. A system of logic 1986, Lincoln-Rembrandt Pub.
33. Koch R. Die aetiologie der tuberkulose. J Mol Med 1932, 11:490-492. 10.1007/BF01765224.
34. Hill A.B. The environment and disease: association or causation?. Proc R Soc Med 1965, 58:295-300.
35. Rothman K. Causes. Am J Epidemiol 1995, 141(2):90.
36. Naranjo C., Busto U., Sellers E., Sandor P., Ruiz I., Roberts E., et al. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther 1981, 30(2):239-245.
37. Gatto N., Campbell U. Redundant causation from a sufficient cause perspective. Epidemiol Perspect Innovat 2010, 7(1):5.
38. Eells E. Probabilistic causality 1991, Cambridge University Press.
39. Good I.J. A causal calculus (I). Br J Philos Sci 1961, XI(44):305-318.
40. Suppes P. A probabilistic theory of causality 1970, North-Holland.
41. Kleinberg S. An Algorithmic Enquiry Concerning Causality. PhD thesis, New York University; 2010.
42. Gaziano J.M. The evolution of population science. JAMA: J Am Med Assoc 2010, 304(20):2288-2289.
43. Susser M. Causal thinking in the health sciences concepts and strategies of epidemiology 1973, Oxford University Press.
44. Susser M. What is a cause and how do we know one? A grammar for pragmatic epidemiology. Am J Epidemiol 1991, 133(7):635.
45. Lewis D. Causation. J Philos 1973, 70:556-567.
46. Woodward J. Probabilistic causality, direct causes and counterfactual dependence. Stochas Causal 2001, 39-63.
47. Russo F., Williamson J. Interpreting causality in the health sciences. Int Stud Philos Sci 2007, 21(2):157-170.
48. Morabia A. On the origin of Hill's causal criteria. Epidemiology 1991, 2(5):367.
49. Thygesen L., Andersen G., Andersen H. A philosophical analysis of the Hill criteria. Br Med J 2005, 59(6):512.
50. Höfler M. The Bradford Hill considerations on causality: a counterfactual perspective. Emerg Themes Epidemiol 2005, 2(1):11.
51. Ward A. The role of causal criteria in causal inferences: Bradford Hill's aspects of association. Epidemiol Perspect Innovat 2009, 6(1):2.
52. Ward A. Causal criteria and the problem of complex causation. Med Health Care Philos 2009, 12(3):333-343.
53. Rothman K., Greenland S. Causation and causal inference in epidemiology. Am J Public Health 2005, 95(S1):S144.
54. Phillips C., Goodman K. The missed lessons of Sir Austin Bradford Hill. Epidemiol Perspect Innovat 2004, 1(1):3.
55. Hausman D.M. Causal relata: tokens, types, or variables?. Erkenntnis 2005, 63(1):33-54.
56. Sober E., Papineau D. Causal factors, causal inference, causal explanation. Proc Aristotelian Soc Suppl Vol 1986, 60:97-136.
57. Woodward J. Making things happen: a theory of causal explanation 2005, Oxford University Press, USA.
58. Reiter R. A theory of diagnosis from first principles. Artif Intell 1987, 32(1):57-95.
59. Bouzid M, Ligeza A. Temporal causal networks for simulation and diagnosis. In: Proceedings of the second IEEE international conference on engineering of complex computer systems, ICECCS, vol. 96; 1996. p. 458-65.
60. Chao C., Yang D., Liu A. An automated fault diagnosis system using hierarchical reasoning and alarm correlation. J Netw Syst Manag 2001, 9(2):183-202.
61. Lunze J., Schiller F. An example of fault diagnosis by means of probabilistic logic reasoning. Control Eng Pract 1999, 7(2):271-278.
62. Rizzi D., Pedersen S. Causality in medicine: towards a theory and terminology. Theor Med Bioeth 1992, 13(3):233-254.
63. Szolovits P., Pauker S. Categorical and probabilistic reasoning in medical diagnosis. Artif Intell 1978, 11(1-2):115-144.
64. Kuipers B. Qualitative simulation as causal explanation. IEEE Trans Syst Man Cybernet 2007, 17(3):432-444.
65. Shibahara T, Tsotsos J, Mylopoulos J, Covvey H. CAA: a knowledge based system using causal knowledge to diagnose cardiac rhythm disorders. In: Proceedings international joint conference on artificial intelligence; 1983.
66. Cooper G. NESTOR: a computer-based medical diagnostic aid that integrates causal and probabilistic knowledge. PhD thesis, Stanford University; 1984.
67. Long W. Temporal reasoning for diagnosis in a causal probabilistic knowledge base. Artif Intell Med 1996, 8(3):193-215.
68. Oniésko A., Lucas P., Druzdzel M. Comparison of rule-based and Bayesian network approaches in medical diagnostic systems. Artif Intell Med 2001, 283-292.
69. Lerner U, Parr R. Inference in hybrid networks: theoretical limits and practical algorithms. In: Proceedings of the 17th conference on uncertainty in artificial intelligence; 2001. p. 310-8.
70. Kleinberg S. A logic for causal inference in time series with discrete and continuous variables. In: Proceedings of the twenty-second international joint conference on artificial intelligence; 2011.
71. Twardy C., Nicholson A., Korb K., McNeil J. Epidemiological data mining of cardiovascular Bayesian networks. Electron J Health Inform 2006, 1(1):e3.
72. Mani S., Cooper G. Causal discovery using a Bayesian local causal discovery algorithm. Proc MedInfo 2004, 731-735.
73. Aliferis C, Tsamardinos I, Statnikov A, Brown L. Causal explorer: a causal probabilistic network learning toolkit for biomedical discovery. In: International conference on mathematics and engineering techniques in medicine and biological sciences (METMBS03), Citeseer; 2003, p. 371-6.
74. Murphy K., et al. The bayes net toolbox for matlab. Comput Sci Stat 2001, 33(2):1024-1034.
75. Glymour C, Scheines R, Spirtes P, Ramsey J. TETRAD IV software; 2004.
76. Simpson E.H. The interpretation of interaction in contingency tables. J R Stat Soc: Ser B (Methodol) 1951, 13(2):238-241.
77. Spirtes P, Meek C, Richardson T. Causal inference in the presence of latent variables and selection bias. In: Proceedings of the eleventh conference on uncertainty in artificial intelligence; 1995. p. 499-506.
78. Cooper G. An overview of the representation and discovery of causal relationships using Bayesian networks. Computation, causation, and discovery 1999, AAAI Press and MIT Press. C. Glymour, G. Cooper (Eds.).
79. Cooper G., Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn 1992, 9(4):309-347.
80. Cartwright N. What is wrong with Bayes nets?. Monist 2003, 84(2).
81. Silva R., Scheine R., Glymour C., Spirtes P. Learning the structure of linear latent variable models. J Mach Learn Res 2006, 7:191-246.
82. Entner D, Hoyer P. On causal discovery from time series data using FCI. In: Probabilistic graphical models; 2010.
83. Zhang J., Spirtes P. Detection of unfaithfulness and robust causal inference. Minds Mach 2008, 18(2):239-271.
84. Halpern J.Y., Pearl J. Causes and explanations: a structural-model approach. Part I: causes. Br J Philos Sci 2005, 56(4):843-887.
85. Murphy K. Dynamic Bayesian networks: representation, inference and learning. PhD thesis, University of California, Berkley; 2002.
86. Zou M., Conzen S. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 2005, 21(1):71.
87. Eldawlatly S., Zhou Y., Jin R., Oweiss K. On the use of dynamic bayesian networks in reconstructing functional neuronal networks from spike train ensembles. Neural Comput 2010, 22(1):158-189.
88. Charitos T., van der Gaag L., Visscher S., Schurink K., Lucas P. A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients. Expert Syst Appl 2009, 36(2):1249-1258.
89. van Gerven M., Taal B., Lucas P. Dynamic Bayesian networks as prognostic models for clinical patient management. J Biomed Inform 2008, 41(4):515-529.
90. Hartemink AJ. Banjo: Bayesian network inference with Java objects; 2008. http://www.cs.duke.edu/amink/software/banjo/.
91. Xiang Z., Minter R., Bi X., Woolf P., He Y. miniTUBA: medical inference by network integration of temporal data using Bayesian analysis. Bioinformatics 2007, 23(18):2423.
92. Robinson J., Hartemink A. Learning non-stationary dynamic bayesian networks. J Mach Learn Res 2010, 9999:3647-3680.
93. Grzegorczyk M., Husmeier D. Non-stationary continuous dynamic bayesian networks. Adv Neural Inform Process Syst 2009, 22:682-690.
94. Granger C.W. Testing for causality: a personal viewpoint. J Econ Dyn Control 1980, 2:329-352.
95. Mukhopadhyay N.D.D., Chatterjee S. Causality and pathway search in microarray time series experiment. Bioinformatics 2007, 23(4):442.
96. Ding M, Chen Y, Bressler SL. Granger causality: basic theory and application to neuroscience; 2006. Arxiv preprint q-bio/0608035.
97. Kamiński M., Ding M., Truccolo W.A., Bressler S.L. Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biol Cybernet 2001, 85(2):145-157.
98. Brandt P. MSBVAR R package version 0.4; 2009.
99. Zou C., Feng J. Granger causality vs. dynamic Bayesian network inference: a comparative study. BMC Bioinform 2009, 10(1):122.
100. Chen Y., Rangarajan G., Feng J., Ding M. Analyzing multiple nonlinear time series with extended Granger causality. Phys Lett A 2004, 324:26-35.
101. Eichler M, Didelez V. Causal reasoning in graphical time series models. In: Proceedings of the 23rd conference on uncertainty in artificial intelligence; 2007.
102. Eichler M. Causal inference from multivariate time series: what can be learned from granger causality. Methodology and philosophy of science 2009, King's College Publications, London. D.W.C. Glymour, W. Wang (Eds.).
103. Agrawal R, Srikant R. Mining sequential patterns. In: Proceedings of the eleventh international conference on data engineering; 1995. p. 3-14.
104. Kleinberg S, Mishra B. The temporal logic of causal structures. In: Proceedings of the 25th conference on uncertainty in artificial intelligence (UAI-09), Corvallis, Oregon; 2009. p. 303-12.
105. Hansson H., Jonsson B. A logic for reasoning about time and reliability. Formal Aspects Comput 1994, 6(5):512-535.
106. Chan K, Poernomo I, Schmidt H, Jayaputera J. A model-oriented framework for runtime monitoring of nonfunctional properties. Lecture notes in computer science, vol. 3712; 2005. p. 38.
107. Benjamini Y., Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat 2001, 29(4):1165-1188.
108. Efron B. Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. J Am Stat Assoc 2004, 99(465):96-105.
109. Kleinberg S., Mishra B. Multiple testing of causal hypotheses. Causality in the sciences 2011, Oxford University Press. P.M. Illari, F. Russo, J. Williamson (Eds.).
110. Kleinberg S, Kolm P, Mishra B. Investigating causal relationships in stock returns with temporal logic based methods; 2010. ArXiv e-prints.
111. Kleinberg S, Mishra B. The Temporal logic of token causes. In: Proceedings of the 12th international conference on the principles of knowledge representation and reasoning (KR2010), Toronto, Canada; May 2010.
112. Guyon I., Aliferis C., Cooper G., Elisseeff A., Pellet J., Spirtes P., et al. Design and analysis of the causation and prediction challenge. JMLR W&CP 2008, 3:1-33.
113. Wright S. The method of path coefficients. Ann Math Stat 1934, 5(3):161-215.
114. Eberhardt F, Scheines R. Interventions and causal inference. In: Proceedings of the 20th biennial meeting of the philosophy of science association; 2006.