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.
CITATION STYLE
Strobl, E. V., & Visweswaran, S. (2019). Markov Blanket Ranking Using Kernel-Based Conditional Dependence Measures (pp. 359–372). https://doi.org/10.1007/978-3-030-21810-2_14
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