Graph_Inference_Dataset
OpenML dataset with id 43286
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102062/Graph_Inference_Dataset.arff
Upload date: 16 March 2022
Dataset Characteristics
Number of features: 101 (numeric: 101, symbolic: 0 and in total binary: 0 )
Number of instances: 500
Number of instances with missing values: 0
Number of missing values: 0
//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, 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 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.
2) 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
(2017-08-24)
This page was built for dataset: Graph_Inference_Dataset
