One of the main algorithms for causal structure learning in Bayesian network is the PC algorithm. The PC algorithm uses conditional independence tests for model selection under the assumption of independent and identically distributed observations. In practice, sample selection in surveys involves more complex sampling designs then the standard test procedure is not valid even asymptotically. In this paper, a modified version of the PC algorithm is proposed for inferring casual structure from complex survey data.

Marella, D., & Vicard, P. (2017). STRUCTURAL LEARNING FOR COMPLEX SURVEY DATA. In Cladag 2017-Book of short papers. Mantova : Universitas Studiorum S.r.l. Casa Editrice.

STRUCTURAL LEARNING FOR COMPLEX SURVEY DATA

MARELLA, Daniela;VICARD, Paola
2017

Abstract

One of the main algorithms for causal structure learning in Bayesian network is the PC algorithm. The PC algorithm uses conditional independence tests for model selection under the assumption of independent and identically distributed observations. In practice, sample selection in surveys involves more complex sampling designs then the standard test procedure is not valid even asymptotically. In this paper, a modified version of the PC algorithm is proposed for inferring casual structure from complex survey data.
978-88-99459-71-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/324205
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