Markov Blanket Ranking using Kernel-based Conditional Dependence Measures
Abstract
Developing feature selection algorithms that move beyond a pure correlational to a more causal analysis of observational data is an important problem in the sciences. Several algorithms attempt to do so by discovering the Markov blanket of a target, but they all contain a forward selection step which variables must pass in order to be included in the conditioning set. As a result, these algorithms may not consider all possible conditional multivariate combinations. We improve on this limitation by proposing a backward elimination method that uses a kernel-based conditional dependence measure to identify the Markov blanket in a fully multivariate fashion. The algorithm is easy to implement and compares favorably to other methods on synthetic and real datasets.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2014
- DOI:
- 10.48550/arXiv.1402.0108
- arXiv:
- arXiv:1402.0108
- Bibcode:
- 2014arXiv1402.0108S
- Keywords:
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- Statistics - Machine Learning;
- Computer Science - Machine Learning
- E-Print:
- 10 pages, 4 figures, 2 algorithms, NIPS 2013 Workshop on Causality, code: github.com/ericstrobl/