{ "data_id": "43298", "name": "pair0004", "exact_name": "pair0004", "version": 3, "version_label": null, "description": "\/\/Add the description.md of the data file pair0004\nCause-effect is a growing database with two-variable cause-effect pairs \ncreated at Max-Planck-Institute for Biological Cybernetics in Tuebingen, Germany.\n==================================================================================================================================================\n\nSome pairs are highdimensional, for machine readability the relevant information about this is coded in Meta-data.\n\nMeta-data contains the following information:\n\nnumber of pair | 1st column of cause | last column of cause | 1st column of effect | last column of effect | dataset weight\n\nThe dataset weight should be used for calculating average performance of causal inference methods\nto avoid a bias introduced by having multiple copies of essentially the same data (for example,\nthe pairs 56-63).\n\nWhen you use this data set in a publication, please cite the following paper (which\nalso contains much more detailed information regarding this data set in the supplement):\n\nJ. M. Mooij, J. Peters, D. Janzing, J. Zscheischler, B. Schoelkopf\n\"Distinguishing cause from effect using observational data: methods and benchmarks\"\nJournal of Machine Learning Research 17(32):1-102, 2016\n\nNOTE: pair0001 - pair0041 are taken from the UCI Machine Learning Repository:\n\nAsuncion, A. & Newman, D.J. (2007). UCI Machine Learning Repository [http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science. \n\n==================================================================================================================================================\nOverview over all data pairs.\n\n\t\t\tvar 1\t\t\t\tvar 2\t\t\t\t\tdataset\t\t\tground truth\n\npair0001\t\tAltitude\t\t\tTemperature\t\t\t\tDWD\t\t\t->\npair0002\t\tAltitude\t\t\tPrecipitation\t\t\t\tDWD\t\t\t->\npair0003\t\tLongitude\t\t\tTemperature\t\t\t\tDWD\t\t\t->\npair0004\t\tAltitude\t\t\tSunshine hours\t\t\t\tDWD\t\t\t->\n\nInformation for pairs0004:\n\nDWD data (Deutscher Wetterdienst)\n\ndata was taken at 349 stations\n\ntaken from\nhttp:\/\/www.dwd.de\/bvbw\/appmanager\/bvbw\/dwdwwwDesktop\/?_nfpb=true&_pageLabel=_dwdwww_klima_umwelt_klimadaten_deutschland&T82002gsbDocumentPath=Navigation%2FOeffentlichkeit%2FKlima__Umwelt%2FKlimadaten%2Fkldaten__kostenfrei%2Fausgabe__mittelwerte__node.html__nnn%3Dtrue\n\nmore recent link (Jan 2010):\nhttp:\/\/www.dwd.de\/bvbw\/appmanager\/bvbw\/dwdwwwDesktop\/?_nfpb=true&_pageLabel=_dwdwww_klima_umwelt_klimadaten_deutschland&T82002gsbDocumentPath=Navigation%2FOeffentlichkeit%2FKlima__Umwelt%2FKlimadaten%2Fkldaten__kostenfrei%2Fausgabe__mittelwerte__node.html__nnn%3Dtrue\n\nmore recent link (Oct 2012):\nhttp:\/\/www.dwd.de\/bvbw\/appmanager\/bvbw\/dwdwwwDesktop?_nfpb=true&_pageLabel=_dwdwww_klima_umwelt_klimadaten_deutschland&T82002gsbDocumentPath=Navigation%2FOeffentlichkeit%2FKlima__Umwelt%2FKlimadaten%2Fkldaten__kostenfrei%2Fausgabe__mittelwerte__akt__node.html%3F__nnn%3Dtrue\n\nx: altitude\n\ny: sunshine (yearly value averaged over 1961-1990)\n\nground truth:\nx --> y", "format": "arff", "uploader": "Oleksandr Zadorozhnyi", "uploader_id": 29044, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-16 10:46:38", "update_comment": null, "last_update": "2022-03-16 10:46:38", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102074\/data.arff", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "pair0004", "\/\/Add the description.md of the data file pair0004 Cause-effect is a growing database with two-variable cause-effect pairs created at Max-Planck-Institute for Biological Cybernetics in Tuebingen, Germany. ================================================================================================================================================== Some pairs are highdimensional, for machine readability the relevant information about this is coded in Meta-data. Meta-data contains the following " ], "weight": 5 }, "qualities": { "NumberOfInstances": 348, "NumberOfFeatures": 2, "NumberOfClasses": null, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 2, "NumberOfSymbolicFeatures": 0, "Dimensionality": 0.005747126436781609, "PercentageOfNumericFeatures": 100, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0, "AutoCorrelation": null, "PercentageOfMissingValues": 0 }, "tags": [ { "uploader": "38960", "tag": "Computer Systems" }, { "uploader": "29044", "tag": "Graphical models" }, { "uploader": "38960", "tag": "Machine Learning" }, { "uploader": "29044", "tag": "MaRDI" }, { "uploader": "29044", "tag": "TA3" } ], "features": [ { "name": "X205", "index": "0", "type": "numeric", "distinct": "263", "missing": "0", "min": "0", "max": "2960", "mean": "333", "stdev": "340" }, { "name": "X1552.0", "index": "1", "type": "numeric", "distinct": "332", "missing": "0", "min": "1097", "max": "1858", "mean": "1536", "stdev": "128" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }