Bayesian network modelling of hierarchical composite indicators

Abstract

The water, sanitation and hygiene (WaSH) sector has witnessed the development of multiple tools for multidimensional monitoring. Hierarchical and composite indicators (CI)-based conceptual frameworks provide one illustrative example. However, this approach does not address the existing interrelationship of the indicators they integrate. Bayesian Networks (BNs) are increasingly exploited to assess WaSH issues and to support planning and decision-making processes. This research aims to evaluate the validity, reliability and feasibility of BNs to replicate an existing CI-based conceptual framework. We adopt a data-driven approach and we propose a semi-automatic methodology. One regional monitoring initiative is selected as a pilot study: the Rural Water Supply and Sanitation Information System (SIASAR). Data from two different countries are processed and analysed to calibrate and validate the model and the method. Major findings show i) an improvement of model inference capacity when providing structure to the networks (according to the CI-based framework), ii) a reduction and quantification of the key components that explain a pre-defined objective variable (implying important advantages in data updating), and iii) an identification of interlinkages among these components (which might enhance multi- and trans-disciplinary actions). We conclude that BNs accurately replicates the CI-based conceptual framework. The proposal contributes to its wider application.