Graph inference datasets. Replication Data for: 'Learning Functional Causal Models with Generative Neural Networks'
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:' Graph datasets in csv format.Used in the article Learning Functional Causal Models with Generative Neural Networks. 1) Each 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 -> effectFile 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 ->' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Causality","Graphical model"]} <|eot_id|>
 Here is the Description of a dataset. Description:' Graph datasets in csv format.Used in the article Learning Functional Causal Models with Generative Neural Networks. 1) Each 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 -> effectFile 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 ->' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
 Graph datasets in csv format.Used in the article Learning Functional Causal Models with Generative Neural Networks. 1) Each 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 -> effectFile 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 ->
Causality; Graphical model