{ "background": "Evaluating the impact of large-scale infrastructure interventions in developing contexts presents significant methodological challenges. Traditional engineering assessments often lack robust counterfactuals, making it difficult to attribute observed performance changes solely to the intervention.", "purpose and objectives": "This case study aimed to develop and apply a quasi-experimental design to rigorously diagnose efficiency gains from a national programme of water treatment facility upgrades. The objective was to isolate the causal effect of the technological interventions from confounding temporal and regional factors.", "methodology": "A difference-in-differences framework was employed, comparing changes in key performance indicators (e.g., turbidity reduction, chlorine contact time) between a treatment group of upgraded facilities and a matched control group of non-upgraded facilities over time. The core statistical model was $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\times \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ captures the causal effect. Inference was based on cluster-robust standard errors at the facility level.", "findings": "The intervention yielded a statistically significant positive effect. The coefficient $\\delta$ indicated that upgraded facilities achieved, on average, a 22% greater reduction in post-treatment turbidity compared to the control group, with a 95% confidence interval of [18%, 26%]. Operational chemical consumption also showed a marked decrease.", "conclusion": "The quasi-experimental design successfully isolated the efficiency gains attributable to the technological upgrades, providing stronger evidence of causality than pre-post analyses alone. The methodology proves viable for engineering performance evaluation in real-world, non-laboratory settings.", "recommendations": "Adopt quasi-experimental frameworks for ex-post evaluation of civil engineering projects to strengthen evidence-based decision-making. Future programmes should incorporate staggered roll-out to facilitate more robust natural experiment designs.", "key words": "quasi-exper