"background": "Evaluating the performance of large-scale water treatment infrastructure in developing regions requires robust methods to isolate the causal effect of interventions from confounding temporal trends. Existing engineering assessments often lack rigorous counterfactual analysis. ", "purpose and objectives": "This article presents a methodological framework for quantifying the causal impact of infrastructure upgrades on system yield. The objective is to provide a replicable, quasi-experimental approach for engineers and planners to assess intervention efficacy. ", "methodology": "We detail a difference-in-differences (DiD) model for panel data from treatment facilities. The core specification is Y{it = \0 + \1 + \2 + \ (\) + \₈ₓ, where \ is the causal estimand. Inference is based on cluster-robust standard errors to account for serial correlation. ", "findings": "As a methodology article, this paper presents no empirical results. The framework's application is illustrated with a hypothetical scenario where the DiD estimator identifies a significant positive yield improvement of approximately 15% attributable to a membrane technology retrofit, with a 95% confidence interval excluding zero. ", "conclusion": "The proposed DiD framework provides a statistically rigorous and practically implementable methodology for performance evaluation in civil engineering contexts, moving beyond simple pre-post comparisons. ", "recommendations": "Adopt the DiD model for ex-post evaluation of water infrastructure projects. Practitioners should prioritise the collection of panel data from both intervention and control facilities to enable robust causal inference. ", "key words": "causal inference, quasi-experimental design, infrastructure evaluation, water treatment, panel data, engineering metrics", "contribution statement": "This paper provides the first formalised application of the difference-in-differences econometric technique to the problem of water treatment plant yield assessment in an African civil engineering context, offering a
Diop et al. (Mon,) studied this question.