Towards an Integral Approach for Modeling Causality

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[1]  Prakash P. Shenoy,et al.  A Bayesian network approach to making inferences in causal maps , 2001, Eur. J. Oper. Res..

[2]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[3]  Frederick Eberhardt,et al.  N-1 Experiments Suffice to Determine the Causal Relations Among N Variables , 2006 .

[4]  Jin Tian,et al.  A general identification condition for causal effects , 2002, AAAI/IAAI.

[5]  Fabio Crestani,et al.  Probability Kinematics in Information Retrieval a case study , 1995 .

[6]  Mikko Koivisto,et al.  Advances in Exact Bayesian Structure Discovery in Bayesian Networks , 2006, UAI.

[7]  Bernard Manderick,et al.  Inference in multi-agent causal models , 2007, Int. J. Approx. Reason..

[8]  Jin Tian,et al.  Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement , 2005, UAI.

[9]  Jesper Tegnér,et al.  Towards scalable and data efficient learning of Markov boundaries , 2007, Int. J. Approx. Reason..

[10]  Bernard Manderick,et al.  Identification in Chain Multi-Agent Causal Models , 2005, FLAIRS Conference.

[11]  B. Manderick,et al.  UnCaDo: Unsure Causal Discovery , 2008 .

[12]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[13]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[14]  James M. Bower,et al.  Computational modeling of genetic and biochemical networks , 2001 .

[15]  Judea Pearl,et al.  Dormant Independence , 2008, AAAI.

[16]  Wesley C. Salmon,et al.  Causality and Explanation , 1998 .

[17]  André Elisseeff,et al.  Using Markov Blankets for Causal Structure Learning , 2008, J. Mach. Learn. Res..

[18]  Kevin Murphy,et al.  Active Learning of Causal Bayes Net Structure , 2006 .

[19]  Kevin P. Murphy,et al.  Exact Bayesian structure learning from uncertain interventions , 2007, AISTATS.

[20]  Paul P. Wang,et al.  Advances to Bayesian network inference for generating causal networks from observational biological data , 2004, Bioinform..

[21]  Tom Burr,et al.  Causation, Prediction, and Search , 2003, Technometrics.

[22]  Nir Friedman,et al.  Being Bayesian about Network Structure , 2000, UAI.

[23]  Yang Xiang,et al.  PROBABILISTIC REASONING IN MULTIAGENT SYSTEMS: A GRAPHICAL MODELS APPROACH, by Yang Xiang, Cambridge University Press, Cambridge, 2002, xii + 294 pp., ISBN 0-521-81308-5 (Hardback, £45.00). , 2002, Robotica.

[24]  Bernard Manderick,et al.  Causal Inference in Multi-Agent Causal Models , 2005, BNAIC.

[25]  Richard Scheines,et al.  Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data , 2000 .

[26]  P. Spirtes,et al.  Ancestral graph Markov models , 2002 .

[27]  Daphne Koller,et al.  Active Learning for Structure in Bayesian Networks , 2001, IJCAI.

[28]  Liviu Badea.  Inferring large gene networks from microarray data: a constraint-based approach , 2003 .

[29]  David Maxwell Chickering,et al.  Optimal Structure Identification With Greedy Search , 2002, J. Mach. Learn. Res..

[30]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[31]  Steffen L. Lauritzen,et al.  Bayesian updating in causal probabilistic networks by local computations , 1990 .

[32]  Multi-Agent Causal Models: Inference and Learning , 2005 .

[33]  Gregory F. Cooper,et al.  Causal Discovery from a Mixture of Experimental and Observational Data , 1999, UAI.

[34]  Christopher Meek,et al.  Causal inference and causal explanation with background knowledge , 1995, UAI.

[35]  Rong Chen,et al.  Learning Bayesian Network Structure from Distributed Data , 2003, SDM.

[36]  Weiru Liu,et al.  Learning belief networks from data: an information theory based approach , 1997, CIKM '97.

[37]  R. Scheines,et al.  Interventions and Causal Inference , 2007, Philosophy of Science.

[38]  R. Khan,et al.  Sequential Tests of Statistical Hypotheses. , 1972 .

[39]  N. Sheehan,et al.  Mendelian randomization as an instrumental variable approach to causal inference , 2007, Statistical methods in medical research.

[40]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[41]  J. Pearl,et al.  Studies in causal reasoning and learning , 2002 .

[42]  Philippe Leray,et al.  An integral approach to causal inference with latent variables , 2007 .

[43]  T. Haavelmo.  The Statistical Implications of a System of Simultaneous Equations , 1943 .

[44]  Wai Lam,et al.  LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE , 1994, Comput. Intell..

[45]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[46]  Jiji Zhang,et al.  A Transformational Characterization of Markov Equivalence for Directed Acyclic Graphs with Latent Variables , 2005, UAI.

[47]  Mikko Koivisto,et al.  Exact Bayesian Structure Discovery in Bayesian Networks , 2004, J. Mach. Learn. Res..

[48]  Fabio Gagliardi Cozman,et al.  Generating Random Bayesian Networks with Constraints on Induced Width , 2004, ECAI.

[49]  B. Shipley.  Cause and correlation in biology , 2000 .

[50]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[51]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[52]  Bernard Manderick,et al.  Causal Graphical Models with Latent Variables: Learning and Inference , 2007, ECSQARU.

[53]  Frederick Eberhardt,et al.  On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables , 2005, UAI.

[54]  David Maxwell Chickering,et al.  Learning Equivalence Classes of Bayesian Network Structures , 1996, UAI.

[55]  K. Sivakumar,et al.  Collective mining of Bayesian networks from distributed heterogeneous data , 2003, Knowledge and Information Systems.

[56]  Bernard Manderick,et al.  Identifiability of causal effects in a multi-agent causal model , 2003, IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003..

[57]  Peter Spirtes,et al.  A Characterization of Markov Equivalence Classes for Ancestral Graphical Models , 2005 .

[58]  Bernard Manderick,et al.  Learning Semi-Markovian Causal Models using Experiments , 2006, Probabilistic Graphical Models.

[59]  Jon Williamson,et al.  Causality and Probability in the Sciences , 2007 .

[60]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[61]  Bernard Manderick,et al.  Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach , 2006, MDAI.

[62]  Thomas S. Richardson,et al.  Causal Inference in the Presence of Latent Variables and Selection Bias , 1995, UAI.

[63]  Didier Dubois,et al.  Possibility theory and statistical reasoning , 2006, Comput. Stat. Data Anal..

[64]  R. W. Robinson.  Counting unlabeled acyclic digraphs , 1977 .

[65]  Jin Tian,et al.  On the Testable Implications of Causal Models with Hidden Variables , 2002, UAI.

[66]  Judea Pearl,et al.  Equivalence and Synthesis of Causal Models , 1990, UAI.

[67]  Jiji Zhang,et al.  Causal Inference and Reasoning in Causally Insu-cient Systems , 2006 .

[68]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .