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 sam- pling 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.
Structural learning for complex survey data / Marella, D.; Vicard, P.. - (2017). (Intervento presentato al convegno Cladag 2017. 11th Scientific Meeting of the Classification and Data Analysis Group tenutosi a Milano).
Structural learning for complex survey data.
Marella D.;Vicard P.
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 sam- pling 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.| File | Dimensione | Formato | |
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