Abstract
{ "background": "Ensuring the reliability of water treatment systems is a critical engineering challenge in developing nations, where infrastructure is often stressed by rapid urbanisation and resource constraints. Systematic, quantitative evaluations of system performance and the impact of interventions are lacking, particularly over extended periods.", "purpose and objectives": "This case study aims to methodologically evaluate the impact of a national rehabilitation programme on the operational reliability of water treatment facilities. Its objective is to provide a robust, quantitative framework for assessing infrastructure improvements in a real-world context.", "methodology": "A quasi-experimental difference-in-differences (DiD) model is employed, comparing treatment facilities that underwent major rehabilitation with a control group of facilities that did not. The core statistical model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where $Y{it}$ is the reliability index. Inference is based on cluster-robust standard errors at the facility level.", "findings": "The analysis indicates a statistically significant positive effect of the rehabilitation programme. The DiD estimator, $\\delta$, shows that rehabilitated facilities achieved a 22 percentage point increase in their mean reliability index compared to the control group, with a 95% confidence interval of [15, 29].", "conclusion": "The applied DiD model proves to be a powerful methodological tool for isolating the causal effect of infrastructure investments on engineering system performance, moving beyond descriptive analysis.", "recommendations": "Adopt quasi-experimental evaluation frameworks in civil engineering project appraisal. Prioritise targeted rehabilitation of components most associated with systemic downtime, as identified by the model. Integrate such analytical models into national asset management strategies.", "key words": "infrastructure reliability, difference-in-differences, causal inference, water treatment, asset management, quasi-experimental design",