"background": "The reliability of water treatment infrastructure in sub-Saharan Africa is a critical determinant of public health and economic development. Current performance assessments often lack robust, diagnostic methodologies capable of isolating causal factors of system failure. ", "purpose and objectives": "This study aimed to develop and apply a novel quasi-experimental methodological framework for the performance diagnostics of water treatment systems, with the objective of quantifying reliability and identifying specific failure mechanisms. ", "methodology": "A longitudinal, interrupted time-series design was employed, monitoring key performance indicators (e. g. , turbidity, chlorine residual) at multiple treatment facilities. System reliability was modelled using a generalised linear mixed model: \ (P (Y{it=1) ) = \0 + \1 Tt + \2 Xit + ui +, where Yit is operational status, Tt is a post-intervention period indicator, Xit are time-varying covariates, and ui are facility random effects. Robust standard errors were used for inference. ", "findings": "The framework successfully diagnosed distinct failure modes. A key finding was that mechanical filtration failures accounted for a significant proportion (approximately 40%) of total system downtime, a relationship confirmed with 95% confidence. Operational performance showed no statistically significant improvement following routine maintenance interventions alone. ", "conclusion": "The proposed quasi-experimental framework provides a rigorous, transferable method for engineering diagnostics, moving beyond descriptive reporting to causal analysis of infrastructure performance. ", "recommendations": "Infrastructure assessments should integrate causal diagnostic methods. Maintenance protocols must be revised to prioritise mechanical filtration components and incorporate predictive, condition-based strategies. ", "key words": "Infrastructure reliability, quasi-experimental design, performance diagnostics, water treatment, sub-Saharan Africa, causal inference", "contribution statement": "This paper presents a novel methodological framework that applies causal inference techniques from econometrics to the field performance evaluation of civil engineering infrastructure, generating actionable diagnostic
Mwinyi et al. (2000) studied this question.