{
    "data_id": "43299",
    "name": "pair0005",
    "exact_name": "pair0005",
    "version": 3,
    "version_label": null,
    "description": "\/\/Add the description.md of the data file pair0005\n\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 pairs0005:\n\nhttps:\/\/archive.ics.uci.edu\/ml\/datasets\/Abalone\n\n1. Title of Database: Abalone data\n\n2. Sources:\n\n   (a) Original owners of database:\n\tMarine Resources Division\n\tMarine Research Laboratories - Taroona\n\tDepartment of Primary Industry and Fisheries, Tasmania\n\tGPO Box 619F, Hobart, Tasmania 7001, Australia\n\t(contact: Warwick Nash +61 02 277277, wnash@dpi.tas.gov.au)\n\n   (b) Donor of database:\n\tSam Waugh (Sam.Waugh@cs.utas.edu.au)\n\tDepartment of Computer Science, University of Tasmania\n\tGPO Box 252C, Hobart, Tasmania 7001, Australia\n\n   (c) Date received: December 1995\n\n3. Attribute information:\n\n   Given is the attribute name, attribute type, the measurement unit and a\n   brief description.  \n\n\tName\t\tData Type\tMeas.\tDescription\n\t----\t\t---------\t-----\t-----------\nx:\tRings\t\tinteger\t\t\t+1.5 gives the age in years\ny:\tLength\t\tcontinuous\tmm\tLongest shell measurement\n\n\nGround truth:\nx --> y",
    "format": "arff",
    "uploader": "Oleksandr Zadorozhnyi",
    "uploader_id": 29044,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-16 10:46:41",
    "update_comment": null,
    "last_update": "2022-03-16 10:46:41",
    "licence": "Public",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102075\/data.arff",
    "default_target_attribute": null,
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "pair0005",
            "\/\/Add the description.md of the data file pair0005 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": 4176,
        "NumberOfFeatures": 2,
        "NumberOfClasses": null,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 2,
        "NumberOfSymbolicFeatures": 0,
        "Dimensionality": 0.0004789272030651341,
        "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": "X15",
            "index": "0",
            "type": "numeric",
            "distinct": "28",
            "missing": "0",
            "min": "1",
            "max": "29",
            "mean": "10",
            "stdev": "3"
        },
        {
            "name": "X0.455",
            "index": "1",
            "type": "numeric",
            "distinct": "134",
            "missing": "0",
            "min": "0",
            "max": "1",
            "mean": "1",
            "stdev": "0"
        }
    ],
    "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
}