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In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships.
Note on the bias of information estimates, 1955
G. A. Miller · 1955
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The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2001
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SciPy: Open source scientific tools for Python, 2001–
Eric Jones, Travis Oliphant, Pearu Peterson, et al · 2001
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Information theoretic measures for clusterings comparison: is a correction for chance necessary?
Nguyen Xuan Vinh, Julien Epps, and James Bailey · 2009
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Cited alongside, same era.
Information-geometric approach to inferring causal directions
Dominik Janzing, Joris Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniušis, Bastian Steudel, and Bernhard Schölkopf · 2012
Cited alongside, same era.
New methods for separating causes from effects in genomics data
Alexander Statnikov, Mikael Henaff, Nikita Lytkin, and Constantin Aliferis · 2012
Later among the works it cites.
Results and analysis of the 2013 chalearn cause-effect pair challenge
Isabelle Guyon · 2014
Later among the works it cites.
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