• Corpus ID: 14003512

Flattening network data for causal discovery : What could wrong ?

@inproceedings{Maier2013FlatteningND,
  title={Flattening network data for causal discovery : What could wrong ?},
  author={Marc E. Maier and Katerina Marazopoulou and David T. Arbour and David Jensen},
  year={2013}
}
Methods for learning causal dependencies from observational data have been the focus of decades of work in social science, statistics, machine learning, and philosophy [9, 10, 11]. Much of the theoretical and practical work on causal discovery has focused on propositional representations. Propositional models effectively represent individual directed causal dependencies (e.g., path analysis, Bayesian networks) or conditional distributions of some outcome variable (e.g., linear regression… 

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