{ "background": "Municipal infrastructure asset management in South Africa faces significant challenges in quantifying the effectiveness of risk reduction interventions. Current evaluation methods often lack rigorous counterfactual analysis, making causal attribution difficult.", "purpose and objectives": "This article presents a methodological framework for the quasi-experimental evaluation of engineering interventions aimed at reducing the risk of municipal infrastructure failure. The objective is to provide a robust statistical model for measuring causal impacts on asset risk metrics.", "methodology": "We develop a difference-in-differences (DiD) model for panel data, comparing treated asset groups (e.g., water pipelines with renewed cathodic protection) with matched control groups over time. The core model is $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\delta (\\text{Treat}i \\cdot \\text{Post}t) + \\epsilon_{it}$, where $\\delta$ is the average treatment effect. Inference relies on cluster-robust standard errors to account for serial correlation.", "findings": "The methodological application demonstrates that the DiD estimator effectively isolates the intervention effect from secular trends. A key finding is that the model's validity hinges on the parallel trends assumption, which can be tested using pre-intervention data. Simulation shows that a violation of this assumption can bias the estimated risk reduction by over 30%.", "conclusion": "The proposed difference-in-differences model provides a rigorous, transferable methodology for evaluating infrastructure risk reduction programmes, offering a substantial improvement over simple before-and-after comparisons.", "recommendations": "Practitioners should adopt this DiD framework for post-implementation reviews of asset management interventions. Future work should integrate engineering degradation models directly into the counterfactual estimation.", "key words": "infrastructure asset management, causal inference, quasi-experimental design, panel data, municipal engineering, risk reduction", "contribution statement": "This paper provides the first formal application