{ "background": "Municipal infrastructure asset systems in Kenya face significant and escalating risks from climate change, population growth, and ageing assets. Current evaluation methods for risk reduction interventions are often descriptive or rely on pre-post comparisons lacking counterfactual rigour, limiting causal inference and robust investment prioritisation.", "purpose and objectives": "This article presents a novel quasi-experimental methodology designed to causally evaluate the effectiveness of engineering interventions aimed at reducing systemic risk within municipal infrastructure asset portfolios. The objective is to provide a structured, evidence-based framework for measuring risk reduction outcomes.", "methodology": "The proposed methodology employs a difference-in-differences design, comparing treated asset groups with matched control groups over time. Key risk metrics, including probability of failure and consequence of failure, are quantified before and after interventions. The core statistical model is $\\Delta Risk{it} = \\beta0 + \\beta1 Treati + \\beta2 Postt + \\beta3 (Treati \\times Postt) + \\epsilon{it}$, where the coefficient $\\beta_3$ captures the causal effect. Inference relies on cluster-robust standard errors to account for spatial correlation within municipal wards.", "findings": "As a methodology article, this paper presents no empirical results. However, the structured framework demonstrates, through a hypothetical application to a road drainage network, that the approach can isolate intervention effects from secular trends. A key directional finding from the illustrative case is that the model successfully attributes a hypothesised 15-20% reduction in flood risk probability to the specific engineering intervention, controlling for seasonal rainfall variations.", "conclusion": "The quasi-experimental design provides a rigorous, transferable framework for evaluating infrastructure risk reduction, moving beyond associative analysis to support causal claims. It addresses a critical gap in asset management practice by enabling evidence-based validation of engineering investments.", "recommendations": "Infrastructure asset managers and researchers should adopt quasi-experimental designs for programme evaluation. Subsequent work should focus on developing standardised risk metrics and