{ "background": "Power-distribution systems in many developing nations face chronic inefficiencies, leading to substantial technical and commercial losses. There is a pressing need for robust, field-based methodologies to quantify the impact of equipment upgrades and interventions within these networks.", "purpose and objectives": "This study aimed to develop and apply a quasi-experimental design to rigorously evaluate the efficiency gains attributable to the deployment of modern power-distribution equipment, specifically smart transformers and composite conductors, within a national utility's network.", "methodology": "A difference-in-differences (DiD) framework was employed, comparing technical loss trajectories in treatment and control groups of feeders over multiple observation periods. 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 $\\delta$ captures the causal effect. Robust standard errors were clustered at the feeder level to account for serial correlation.", "findings": "The intervention yielded a statistically significant reduction in average technical losses of 4.7 percentage points (95% CI: 3.1 to 6.3). This effect was robust to multiple model specifications and indicates a substantial improvement in network efficiency directly linked to the equipment upgrade programme.", "conclusion": "The quasi-experimental design provides a credible method for isolating the effect of engineering interventions in complex, real-world distribution networks. The results confirm that targeted equipment modernisation can deliver significant efficiency gains.", "recommendations": "Utilities should adopt rigorous, quasi-experimental evaluation frameworks for future capital projects to validate engineering assumptions and prioritise investments. Regulatory frameworks should incentivise efficiency improvements measured through such empirical approaches.", "key words": "quasi-experimental design, power distribution, technical losses, difference-in-differences, causal inference, network efficiency", "contribution statement": "This paper provides the first