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008 191022s2019 sz | s |||| 0|eng d
020 _a9783030218102
_9978-3-030-21810-2
024 7 _a10.1007/978-3-030-21810-2
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aCause Effect Pairs in Machine Learning
_h[electronic resource] /
_cedited by Isabelle Guyon, Alexander Statnikov, Berna Bakir Batu.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXVI, 372 p. 122 illus., 90 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aThe Springer Series on Challenges in Machine Learning,
_x2520-1328
505 0 _a1. The cause-effect problem: motivation, ideas, and popular misconceptions -- 2. Evaluation methods of cause-effect pairs -- 3. Learning Bivariate Functional Causal Models -- 4. Discriminant Learning Machines -- 5. Cause-Effect Pairs in Time Series with a Focus on Econometrics -- 6. Beyond cause-effect pairs -- 7. Results of the Cause-Effect Pair Challenge -- 8. Non-linear Causal Inference using Gaussianity Measures -- 9. From Dependency to Causality: A Machine Learning Approach -- 10. Pattern-based Causal Feature Extraction -- 11. Training Gradient Boosting Machines using Curve-fitting and Information-theoretic Features for Causal Direction Detection -- 12. Conditional distribution variability measures for causality detection -- 13. Feature importance in causal inference for numerical and categorical variables -- 14. Markov Blanket Ranking using Kernel-based Conditional Dependence Measures.
520 _aThis book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.
650 0 _aArtificial intelligence.
650 0 _aComputer vision.
650 0 _aPattern recognition systems.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputer Vision.
650 2 4 _aAutomated Pattern Recognition.
700 1 _aGuyon, Isabelle.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aStatnikov, Alexander.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBatu, Berna Bakir.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030218096
776 0 8 _iPrinted edition:
_z9783030218119
776 0 8 _iPrinted edition:
_z9783030218126
830 0 _aThe Springer Series on Challenges in Machine Learning,
_x2520-1328
856 4 0 _uhttps://doi.org/10.1007/978-3-030-21810-2
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c185592
_d185592