"background": "Water treatment systems in Ghana face persistent challenges in operational efficiency and yield optimisation. Existing performance assessments often lack rigorous causal identification, relying on observational data susceptible to confounding factors. This methodological gap limits the ability to isolate the true effect of specific interventions on system output. ", "purpose and objectives": "This working paper proposes and evaluates a quasi-experimental framework for diagnosing yield improvements in water treatment facilities. Its primary objective is to establish a robust methodological approach for attributing changes in treated water volume to targeted engineering interventions, moving beyond correlational analysis. ", "methodology": "We employ a difference-in-differences (DiD) design, comparing yield trends in treatment plants receiving a specified coagulant dosing intervention against a matched control group. The core statistical model is Y{it = \0 + \1 + \2 + \ (\) + \₈ₓ, where \ is the causal parameter of interest. Inference is based on cluster-robust standard errors at the plant level. ", "findings": "The diagnostic framework, applied to a sample of facilities, indicates a positive and statistically significant average treatment effect on the treated (ATT). A key concrete result is an estimated yield increase of approximately 12% (95% CI: 8% to 16%) attributable to the optimised dosing protocol. The analysis also identifies system age as a significant moderator of treatment effect heterogeneity. ", "conclusion": "The quasi-experimental design provides a more causally credible method for evaluating engineering interventions in water treatment systems than prior descriptive approaches. It successfully isolates the impact of specific process changes from secular trends and other contemporaneous factors. ", "recommendations": "Adoption of this diagnostic framework is recommended for future performance evaluations and pilot studies within the sector. Engineers and plant managers should prioritise the collection of high-frequency operational data to facilitate such rigorous impact
Asante et al. (Wed,) studied this question.