Replacing Causal Faithfulness with Algorithmic Independence of Conditionals
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[1] Ray J. Solomonoff,et al. A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..
[2] Ray J. Solomonoff,et al. A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..
[3] Gregory J. Chaitin,et al. On the Length of Programs for Computing Finite Binary Sequences , 1966, JACM.
[4] A. Kolmogorov. Three approaches to the quantitative definition of information , 1968 .
[5] G. Chaitin. A Theory of Program Size Formally Identical to Information Theory , 1975, JACM.
[6] P. Spirtes,et al. Causation, prediction, and search , 1993 .
[7] Christopher Meek,et al. Strong completeness and faithfulness in Bayesian networks , 1995, UAI.
[8] Michael I. Jordan. Graphical Models , 1998 .
[9] N. Cartwright. The dappled world : a study of the boundaries of science , 1999 .
[10] J. Pearl. Causality: Models, Reasoning and Inference , 2000 .
[11] Péter Gács,et al. Algorithmic statistics , 2000, IEEE Trans. Inf. Theory.
[12] R. Solomonoff. A PRELIMINARY REPORT ON A GENERAL THEORY OF INDUCTIVE INFERENCE , 2001 .
[13] Nancy Cartwright,et al. Against Modularity, the Causal Markov Condition, and Any Link Between the Two: Comments on Hausman and Woodward , 2002, The British Journal for the Philosophy of Science.
[14] S. Lauritzen,et al. Chain graph models and their causal interpretations , 2002 .
[15] R. Giere. The Dappled World: A Study of the Boundaries of Science , 2003 .
[16] J. Lemeire,et al. Causal Models as Minimal Descriptions of Multivariate Systems , 2006 .
[17] Kevin B. Korb,et al. The power of intervention , 2006, Minds and Machines.
[18] P. Grünwald. The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .
[19] Marcus Hutter,et al. On Universal Prediction and Bayesian Confirmation , 2007, Theor. Comput. Sci..
[20] P. Grünwald. The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .
[21] Bernhard Schölkopf,et al. Nonlinear causal discovery with additive noise models , 2008, NIPS.
[22] B. Schoelkopf,et al. Distinguishing Cause and Effect via Second Order Exponential Models , 2009, 0910.5561.
[23] Dominik Janzing,et al. Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory , 2009, Open Syst. Inf. Dyn..
[24] Bernhard Schölkopf,et al. Telling cause from effect based on high-dimensional observations , 2009, ICML.
[25] Bernhard Schölkopf,et al. Inferring deterministic causal relations , 2010, UAI.
[26] Jiji Zhang,et al. Intervention, determinism, and the causal minimality condition , 2011, Synthese.
[27] Bernhard Schölkopf,et al. Causal Inference Using the Algorithmic Markov Condition , 2008, IEEE Transactions on Information Theory.
[28] Dominik Janzing,et al. Testing whether linear equations are causal: A free probability theory approach , 2011, UAI.
[29] Marcus Hutter,et al. A Philosophical Treatise of Universal Induction , 2011, Entropy.
[30] Bernhard Schölkopf,et al. Causal Inference on Discrete Data Using Additive Noise Models , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[31] Abdellah Touhafi,et al. When are graphical causal models not good models , 2011 .
[32] Jan Lemeire,et al. Inferring the causal decomposition under the presence of deterministic relations , 2011, ESANN.
[33] Jon Williamson,et al. Causality in the Sciences , 2011 .
[34] Bernhard Schölkopf,et al. Identifiability of Causal Graphs using Functional Models , 2011, UAI.
[35] Bernhard Schölkopf,et al. Information-geometric approach to inferring causal directions , 2012, Artif. Intell..