A Theoretical Study of Y Structures for Causal Discovery
By Subramani Mani, Gregory F. Cooper, Peter Spirtes. 2006
68 citations recorded by the source corpus.
There are several existing algorithms that under appropriate assumptions can reliably identify a subset of the underlying causal relationships from observational data. This paper introduces the first computationally feasible score-based algorithm that can reliably identify causal relationships in the large sample limit for discrete models, while allowing for the possibility that there are unobserved common causes. In doing so, the algorithm does not ever need to assign scores to causal structures with unobserved common causes. The algorithm is based on the identification of so called Y substructures within Bayesian network structures that can be learned from observational data. An example of a Y substructure is A -> C, B -> C, C -> D. After providing background on causal discovery, the paper proves the conditions under which the algorithm is reliable in the large sample limit.
Subfields: Artificial Intelligence, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Joris M. Mooij
- Jerome Cremers
- Robin J. Evans
- Ricardo M. A. Silva
- Jan Peters
- Bernhard Schölkopf
- Dominik Janzing
- Peter Buhlmann
In Management Science and Operations Research
Citations in the corpus
- Causal Inference through a Witness Protection Program (2014, background): “Much of the machinery here developed, including Entner’s Rules, can be adapted to the case where causal ordering is unknown: the search for “Y-structures” [10] generalizes the chain structure search to this case.”
- An empirical study of one of the simplest causal prediction algorithms (2015, methodology): “That special case is closely related to an existing method to detect so-called Ystructures [Mani et al., 2006].”