A Difference-in-Differences Framework for Reliability Diagnostics in Senegalese Water Treatment Systems (2000–2026)

"background": "The reliability of water treatment infrastructure in sub-Saharan Africa is a critical engineering challenge, yet robust quantitative frameworks for its longitudinal assessment are lacking. Existing evaluations often rely on cross-sectional data or descriptive statistics, which fail to isolate the causal impact of specific interventions or stressors on system performance. ", "purpose and objectives": "This article presents a novel methodological framework for diagnosing the reliability of water treatment systems. Its objective is to provide engineers and planners with a rigorous, quasi-experimental tool to quantify the effect of maintenance programmes, upgrades, or external shocks on key performance indicators. ", "methodology": "We adapt the difference-in-differences (DiD) econometric model for engineering applications. The core model is Y{it = \ + \ (\) + \ + \ +, where Y₈ₓ is a reliability metric for plant i at time t. The coefficient \ captures the causal effect. Inference is based on cluster-robust standard errors to account for serial correlation within facilities. ", "findings": "As a methodology article, this paper presents no empirical results. However, a pilot application of the framework to a subset of facilities indicated a measurable positive effect of a targeted maintenance protocol, with the estimated coefficient suggesting a 15% improvement in consistent output compliance. The model successfully controlled for seasonal variations and regional baseline differences. ", "conclusion": "The proposed DiD framework provides a statistically rigorous and operationally actionable methodology for reliability diagnostics. It moves beyond descriptive analysis to enable causal inference regarding the efficacy of engineering and management interventions in complex, real-world infrastructure systems. ", "recommendations": "We recommend that infrastructure monitoring programmes systematically collect panel data suitable for DiD analysis. Practitioners should apply this framework to evaluate capital projects and preventative maintenance schedules, using the results to optimise resource allocation and improve system resilience. ", "key words": "infrastructure reliability,