The association structure of a Bayesian network can be drawn based on subject matter or experts knowledge, or have to be learned from a database. In case of data driven learning, one of the most known procedures is the PC algorithm that 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 modified version of the PC algorithm is proposed for inferring casual structure from complex survey data. Finally, a simulation experiment is performed.
Vicard, P. (2023). Causal discovery for complex survey data. In Statistical Learning, Sustainability and Impact Evaluation. Book of the short papers (pp.15-20). Pearson.
Causal discovery for complex survey data
Paola Vicard
2023-01-01
Abstract
The association structure of a Bayesian network can be drawn based on subject matter or experts knowledge, or have to be learned from a database. In case of data driven learning, one of the most known procedures is the PC algorithm that 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 modified version of the PC algorithm is proposed for inferring casual structure from complex survey data. Finally, a simulation experiment is performed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
