SpaCE: The Spatial Confounding Environment
<|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:'SpaCE: The Spatial Confounding Environment is a benchmarking dataset for causal inference incorporating spatial structure. In particular, SpaCE datasets contain real confounder and exposure/treatment data inspired by environmental health studies. The synthetic outcome and counterfactual are generated according to recommended practices for causal evaluation by mimicking the real outcome data distribution learned with machine learning and neural network methods. Spatial confounding is achieved by masking influential confounders in the learned model' . 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" : ["spatial confounding, semi-synthetic data, neural networks"]} <|eot_id|>
 Here is the Description of a dataset. Description:'SpaCE: The Spatial Confounding Environment is a benchmarking dataset for causal inference incorporating spatial structure. In particular, SpaCE datasets contain real confounder and exposure/treatment data inspired by environmental health studies. The synthetic outcome and counterfactual are generated according to recommended practices for causal evaluation by mimicking the real outcome data distribution learned with machine learning and neural network methods. Spatial confounding is achieved by masking influential confounders in the learned model' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
SpaCE: The Spatial Confounding Environment is a benchmarking dataset for causal inference incorporating spatial structure. In particular, SpaCE datasets contain real confounder and exposure/treatment data inspired by environmental health studies. The synthetic outcome and counterfactual are generated according to recommended practices for causal evaluation by mimicking the real outcome data distribution learned with machine learning and neural network methods. Spatial confounding is achieved by masking influential confounders in the learned model
spatial confounding, semi-synthetic data, neural networks