PC COMPLEX: PC ALGORITHM FOR COMPLEX SURVEY DATA

Daniela Marella

No 240, Departmental Working Papers of Economics - University 'Roma Tre' from Department of Economics - University Roma Tre

Abstract: PC algorithm is one of the most known procedures for Bayesian networks structural learning. The structure is inferred carrying out several independence tests on a database and building a Bayesian network in agreement with the tests results. The PC algorithm is based on 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 order to avoid misleading results about the true causal structure the sample selection process must be taken into account in the structural learning process. In this paper, a modi ed version of the PC algorithm is proposed for inferring casual structure from complex survey data. It is based on resampling techniques for nite population. A simulation experiment showing the robustness with respect to departures from the assumptions and the good performance of the proposed algorithm is carried out.

Keywords: Bayesian network; complex survey data; pseudo-population; structural learning. (search for similar items in EconPapers)
JEL-codes: C10 C12 C18 C83 (search for similar items in EconPapers)
Pages: 27
Date: 2018-07
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:rtr:wpaper:0240

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