Discovering Causal Relations in the Presence of Latent Confounders

[1]  F. Fisher A Correspondence Principle for Simultaneous Equation Models , 1970 .

[2]  David Maxwell Chickering,et al.  Learning Bayesian Networks is , 1994 .

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

[4]  Yuichiro Kitajima,et al.  Causation and Intervention in Algebraic Quantum Field Theory , 2010 .

[5]  Frederick Eberhardt,et al.  Almost Optimal Intervention Sets for Causal Discovery , 2008, UAI.

[6]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[7]  Peter Bühlmann,et al.  Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract) , 2011, UAI.

[8]  D. Danks Scientific Coherence and the Fusion of Experimental Results , 2005, The British Journal for the Philosophy of Science.

[9]  J. Woodward,et al.  Independence, Invariance and the Causal Markov Condition , 1999, The British Journal for the Philosophy of Science.

[10]  Keith A. Markus,et al.  Making Things Happen: A Theory of Causal Explanation , 2007 .

[11]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[12]  Tomi Silander,et al.  A Simple Approach for Finding the Globally Optimal Bayesian Network Structure , 2006, UAI.

[13]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[14]  R. Mirman The direction of time , 1975 .

[15]  Peter Bühlmann,et al.  Two optimal strategies for active learning of causal models from interventional data , 2012, Int. J. Approx. Reason..

[16]  David Danks,et al.  Integrating Locally Learned Causal Structures with Overlapping Variables , 2008, NIPS.

[17]  Thomas S. Richardson,et al.  Automated discovery of linear feedback models , 1996 .

[18]  T. Speed,et al.  On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9 , 1990 .

[19]  Bernhard Schölkopf,et al.  Regression by dependence minimization and its application to causal inference in additive noise models , 2009, ICML '09.

[20]  Patrik O. Hoyer,et al.  Discovering Unconfounded Causal Relationships Using Linear Non-Gaussian Models , 2010, JSAI-isAI Workshops.

[21]  Patrik O. Hoyer,et al.  Estimation of causal effects using linear non-Gaussian causal models with hidden variables , 2008, Int. J. Approx. Reason..

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

[23]  J. Koster,et al.  Markov properties of nonrecursive causal models , 1996 .

[24]  Jiji Zhang,et al.  On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias , 2008, Artif. Intell..

[25]  Tommi S. Jaakkola,et al.  Learning Bayesian Network Structure using LP Relaxations , 2010, AISTATS.

[26]  Yun Peng,et al.  Plausibility of Diagnostic Hypotheses: The Nature of Simplicity , 1986, AAAI.

[27]  Bernhard Schölkopf,et al.  Kernel-based Conditional Independence Test and Application in Causal Discovery , 2011, UAI.

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

[29]  C. Meek,et al.  Graphical models: selecting causal and statistical models , 1997 .

[30]  Bernhard Schölkopf,et al.  On Causal Discovery with Cyclic Additive Noise Models , 2011, NIPS.

[31]  Aapo Hyvärinen,et al.  A Linear Non-Gaussian Acyclic Model for Causal Discovery , 2006, J. Mach. Learn. Res..

[32]  Visa Koivunen,et al.  Identifiability, separability, and uniqueness of linear ICA models , 2004, IEEE Signal Processing Letters.

[33]  Aapo Hyvärinen,et al.  Causal discovery of linear acyclic models with arbitrary distributions , 2008, UAI.

[34]  Giorgos Borboudakis,et al.  A constraint-based approach to incorporate prior knowledge in causal models , 2011, ESANN.

[35]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

[36]  D. Rubin [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies , 1990 .

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

[38]  Vincenzo Lagani,et al.  Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies , 2012, J. Mach. Learn. Res..

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

[40]  J. I The Design of Experiments , 1936, Nature.

[41]  D. Rubin Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[42]  Bernhard Schölkopf,et al.  Nonlinear causal discovery with additive noise models , 2008, NIPS.

[43]  Ioannis G. Tollis,et al.  Learning Causal Structure from Overlapping Variable Sets , 2010, AISTATS.

[44]  David Heckerman,et al.  Learning Gaussian Networks , 1994, UAI.

[45]  Carl E. Rasmussen,et al.  Occam's Razor , 2000, NIPS.

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

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

[48]  A. Philip Dawid,et al.  Causality : statistical perspectives and applications , 2012 .

[49]  David Danks,et al.  Learning the Causal Structure of Overlapping Variable Sets , 2002, Discovery Science.

[50]  Giorgos Borboudakis,et al.  Incorporating Causal Prior Knowledge as Path-Constraints in Bayesian Networks and Maximal Ancestral Graphs , 2012, ICML.

[51]  Nir Friedman,et al.  The Bayesian Structural EM Algorithm , 1998, UAI.

[52]  Fabio Gagliardi Cozman,et al.  Axiomatizing Noisy-OR , 2004, ECAI.

[53]  Mark W. Schmidt,et al.  Modeling Discrete Interventional Data using Directed Cyclic Graphical Models , 2009, UAI.

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

[55]  Peter Spirtes,et al.  Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables , 2011, AISTATS.

[56]  D. A. Kenny,et al.  Correlation and Causation , 1937, Wilmott.

[57]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[58]  Judea Pearl,et al.  A Theory of Inferred Causation , 1991, KR.

[59]  Le Song,et al.  A Kernel Statistical Test of Independence , 2007, NIPS.

[60]  Thomas S. Richardson,et al.  Maximum likelihood fitting of acyclic directed mixed graphs to binary data , 2010, UAI.

[61]  Frederick Eberhardt,et al.  Combining Experiments to Discover Linear Cyclic Models with Latent Variables , 2010, AISTATS.

[62]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[63]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[64]  Frederick Eberhardt,et al.  Sufficient Condition for Pooling Data from different Distributions , 2006 .

[65]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[66]  J. D. Toma,et al.  A Response to the Rejoinder , 2003 .

[67]  Christopher Meek,et al.  Strong completeness and faithfulness in Bayesian networks , 1995, UAI.

[68]  Yangbo He,et al.  Active Learning of Causal Networks with Intervention Experiments and Optimal Designs , 2008 .

[69]  Thomas S. Richardson,et al.  Iterative Conditional Fitting for Gaussian Ancestral Graph Models , 2004, UAI.

[70]  R. P. McDonald,et al.  Structural Equations with Latent Variables , 1989 .

[71]  Aapo Hyvärinen,et al.  Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders , 2012, ICANN.

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

[73]  Rina Dechter,et al.  Identifying Independencies in Causal Graphs with Feedback , 1996, UAI.

[74]  Franklin M. Fisher,et al.  The identification problem in econometrics , 1967 .

[75]  Tom Heskes,et al.  Causal discovery in multiple models from different experiments , 2010, NIPS.

[76]  C. Glymour,et al.  STATISTICS AND CAUSAL INFERENCE , 1985 .

[77]  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.

[78]  Zoubin Ghahramani,et al.  The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models , 2009, J. Mach. Learn. Res..

[79]  P. Suppes A Probabilistic Theory Of Causality , 1970 .

[80]  Aapo Hyvärinen,et al.  On the Identifiability of the Post-Nonlinear Causal Model , 2009, UAI.

[81]  Elliott Sober,et al.  Venetian Sea Levels, British Bread Prices, and the Principle of the Common Cause , 2001, The British Journal for the Philosophy of Science.

[82]  A. Dawid Conditional Independence in Statistical Theory , 1979 .

[83]  J. Woodward,et al.  Scientific Explanation and the Causal Structure of the World , 1988 .

[84]  P. Games Correlation and Causation: A Logical Snafu , 1990 .

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

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

[87]  Richard Scheines,et al.  Learning the Structure of Linear Latent Variable Models , 2006, J. Mach. Learn. Res..

[88]  Peter Spirtes,et al.  Directed Cyclic Graphical Representations of Feedback Models , 1995, UAI.

[89]  Constantin F. Aliferis,et al.  The max-min hill-climbing Bayesian network structure learning algorithm , 2006, Machine Learning.

[90]  O. Penrose The Direction of Time , 1962 .

[91]  A. Philip Dawid,et al.  Beware of the DAG! , 2008, NIPS Causality: Objectives and Assessment.

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

[93]  Peter Bühlmann,et al.  Two Optimal Strategies for Active Learning of Causal Models from Interventions , 2012, ArXiv.

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

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

[96]  Tom Heskes,et al.  A Bayesian Approach to Constraint Based Causal Inference , 2012, UAI.

[97]  Aapo Hyvärinen,et al.  DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model , 2011, J. Mach. Learn. Res..

[98]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

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

[100]  J. Robins,et al.  On the impossibility of inferring causation from association without background knowledge , 1999 .

[101]  David Heckerman,et al.  A New Look at Causal Independence , 1994, UAI.

[102]  Patrik O. Hoyer,et al.  Discovering Cyclic Causal Models by Independent Components Analysis , 2008, UAI.

[103]  Radford M. Neal On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note) , 2000, J. Artif. Intell. Res..

[104]  Bernhard Schölkopf,et al.  Identifiability of Causal Graphs using Functional Models , 2011, UAI.