DEVELOPMENT... { "data_id": "43278", "name": "Graph_Inference_Dataset", "exact_name": "Graph_Inference_Dataset", "version": 1, "version_label": null, "description": "\/\/Add the description.md of the data file Graph_Inference_Dataset\n\nGoudet, Olivier, 2017, \"Graph inference datasets. Replication Data for: \"Learning Functional Causal Models with Generative Neural Networks\"\",\n\n[Link](https:\/\/doi.org\/10.7910\/DVN\/UZMB69), Harvard Dataverse, V1, UNF:6:wrgpGhxTNPqE4R5S2cNcpg== [fileUNF] \n\n\nGraph datasets in csv format. Used in the article Learning Functional Causal Models with Generative Neural Networks.\n1) File *_numdata.csv contain the data of around 20 variables connected in a graph without hidden variables. G2, G3, G4 and G5 refered to graph with 2, 3, 4 and 5 parents maximum for each node. Each file *_target.csv contains the ground truth of the graph with cause -> effect File beginning by \"Big\" are larger graphs with 100 variables.\n2) Each file *_confounders_numdata.csv contain the data of around 20 variables connected in a graph. There are 3 hidden variables. Each file *_confounders_skeleton.csv contains the skeleton of the graph (including spurious links due to common hidden cause). Each file *_confounders_target.csv contains the ground truth of the graph with the direct visible cause -> effect. The task is to recover the direct visible links cause->effect while removing the spurious links of the skeleton\n(2017-08-24)", "format": "arff", "uploader": "Robin Wise", "uploader_id": 29044, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-15 22:03:14", "update_comment": null, "last_update": "2022-03-15 22:03:14", "licence": "Public", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102053\/data.arff", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "Graph_Inference_Dataset", "\/\/Add the description.md of the data file Graph_Inference_Dataset Goudet, Olivier, 2017, \"Graph inference datasets. Replication Data for: \"Learning Functional Causal Models with Generative Neural Networks\"\", [Link](https:\/\/doi.org\/10.7910\/DVN\/UZMB69), Harvard Dataverse, V1, UNF:6:wrgpGhxTNPqE4R5S2cNcpg== [fileUNF] Graph datasets in csv format. Used in the article Learning Functional Causal Models with Generative Neural Networks. 1) File *_numdata.csv contain the data of around 20 variables conne " ], "weight": 5 }, "qualities": { "NumberOfInstances": 500, "NumberOfFeatures": 101, "NumberOfClasses": null, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 101, "NumberOfSymbolicFeatures": 0, "PercentageOfSymbolicFeatures": 0, "AutoCorrelation": null, "PercentageOfNumericFeatures": 100, "PercentageOfMissingValues": 0, "PercentageOfInstancesWithMissingValues": 0, "PercentageOfBinaryFeatures": 0, "NumberOfBinaryFeatures": 0, "MinorityClassSize": null, "MinorityClassPercentage": null, "MajorityClassSize": null, "MajorityClassPercentage": null, "Dimensionality": 0.202 }, "tags": [], "features": [ { "name": "V0", "index": "0", "type": "numeric", "distinct": "500", "missing": "0", "min": "-1", "max": "1", "mean": "0", "stdev": "1" }, { "name": "V1", "index": "1", "type": "numeric", 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