• Corpus ID: 219558684

Active Invariant Causal Prediction: Experiment Selection through Stability

@article{Gamella2020ActiveIC,
  title={Active Invariant Causal Prediction: Experiment Selection through Stability},
  author={Juan L Gamella and Christina Heinze-Deml},
  journal={arXiv: Methodology},
  year={2020}
}
A fundamental difficulty of causal learning is that causal models can generally not be fully identified based on observational data only. Interventional data, that is, data originating from different experimental environments, improves identifiability. However, the improvement depends critically on the target and nature of the interventions carried out in each experiment. Since in real applications experiments tend to be costly, there is a need to perform the right interventions such that as… 
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23 Citations
Highly Influential Citations
4
Background Citations
9
Methods Citations
6
Results Citations
2

23 Citations

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