{ "background": "Evaluating the cost-effectiveness of water treatment infrastructure in developing nations remains a significant challenge, often hindered by a lack of controlled experimental conditions and longitudinal operational data. Existing assessments frequently rely on cross-sectional data, which fail to account for temporal variations and confounding factors inherent in complex engineering systems.", "purpose and objectives": "This case study presents and applies a novel quasi-experimental design to diagnose the cost-effectiveness of public water treatment systems. The primary objective is to demonstrate a robust methodological framework capable of isolating the causal impact of specific operational interventions on treatment costs and output quality.", "methodology": "A difference-in-differences (DiD) framework was employed, analysing panel data from treatment plants grouped into intervention and control cohorts based on infrastructure upgrades. The core statistical model is $Cost{it} = \\alpha + \\beta1 (Treati \\cdot Postt) + \\gamma X{it} + \\deltai + \\lambdat + \\epsilon{it}$, where $\\deltai$ and $\\lambdat$ are plant and year fixed effects. Robust standard errors were clustered at the plant level to account for serial correlation.", "findings": "The analysis indicates that the cohort receiving advanced filtration upgrades achieved a 22% reduction in normalised chemical dosing costs relative to the control group, with the DiD estimator significant at the 95% confidence level. However, this cost benefit was partially offset by a 15% increase in energy consumption for backwashing cycles, revealing a critical trade-off in operational efficiency.", "conclusion": "The quasi-experimental design proved viable for causal diagnostics in a real-world engineering context, moving beyond descriptive benchmarking. It successfully quantified the nuanced financial and operational trade-offs associated with a specific technological intervention in water treatment.", "recommendations": "Infrastructure planners should adopt such causal diagnostic frameworks prior to large-scale technology rollouts. Furthermore, operational protocols for new filtration systems must be optimised to mitigate identified increases in energy intensity to realise net lifecycle cost savings.", "