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Pc Complex: Pc Algorithm For Complex Survey Data

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  • Daniela Marella

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.

Suggested Citation

  • Daniela Marella, 2018. "Pc Complex: Pc Algorithm For Complex Survey Data," Departmental Working Papers of Economics - University 'Roma Tre' 0240, Department of Economics - University Roma Tre.
    • Handle: RePEc:rtr:wpaper:0240
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    References listed on IDEAS

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    1. Marco Di Zio & Mauro Scanu & Lucia Coppola & Orietta Luzi & Alessandra Ponti, 2004. "Bayesian networks for imputation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 309-322, May.
    2. Pier Luigi Conti & Daniela Marella, 2015. "Inference for Quantiles of a Finite Population: Asymptotic versus Resampling Results," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 545-561, June.
    3. Kalisch, Markus & Mächler, Martin & Colombo, Diego & Maathuis, Marloes H. & Bühlmann, Peter, 2012. "Causal Inference Using Graphical Models with the R Package pcalg," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i11).
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    Cited by:

    1. Pier Luigi Conti & Alberto Iorio & Alessio Guandalini & Daniela Marella & Paola Vicard & Vincenzina Vitale, 2020. "On the estimation of the Lorenz curve under complex sampling designs," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 1-24, March.

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    More about this item

    Keywords

    Bayesian network; complex survey data; pseudo-population; structural learning.;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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