# 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…

## 3 Citations

Non-Parametric Inference of Relational Dependence

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A consistent, non-parametric, scalable kernel test is proposed to operationalize the relational independence test for non-i.i.d. observational data under a set of structural assumptions and is empirically evaluated on a variety of synthetic and semi-synthetic networks.

A new approach to a legacy concern: Evaluating machine-learned Bayesian networks to predict childhood lead exposure risk from community water systems.

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Causal Discovery for Relational Domains: Representation, Reasoning, and Learning

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This chapter discusses the role of language, representation, andreasoning in the development of knowledge in the context of international relations.

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