Abstract
{ "background": "Evaluating the long-term cost-effectiveness of water treatment infrastructure in low-resource settings remains a significant methodological challenge, particularly for isolating the causal impact of specific interventions from confounding factors.", "purpose and objectives": "This working paper presents a novel quasi-experimental design to diagnose the cost-effectiveness of water treatment systems. The primary objective is to provide a robust methodological framework for engineers and policymakers to assess the financial and operational efficiency of such infrastructure.", "methodology": "The proposed design employs a difference-in-differences (DiD) approach, leveraging phased implementation across districts. The core statistical model is $Y{dt} = \\beta0 + \\beta1 \\text{Treat}{dt} + \\beta2 \\text{Post}{dt} + \\beta3 (\\text{Treat} \\times \\text{Post}){dt} + \\gamma X{dt} + \\epsilon{dt}$, where $Y{dt}$ is the cost-effectiveness ratio for district $d$ at time $t$. Inference relies on cluster-robust standard errors at the district level.", "findings": "This is a methodological paper; no empirical results from the full study period are yet available. Preliminary analysis of a pilot subset indicates a strong positive direction for the key interaction coefficient $\\beta3$, suggesting the intervention improves cost-effectiveness. The associated 95% confidence interval, however, is wide, underscoring the need for the full longitudinal dataset.", "conclusion": "The developed quasi-experimental framework provides a viable and rigorous method for causal inference in infrastructure performance evaluation, moving beyond descriptive cost-benefit analysis.", "recommendations": "We recommend the adoption of this DiD design for future engineering evaluations of public health infrastructure. Policymakers should mandate the collection of granular, time-variant operational data to facilitate such analyses.", "key words": "quasi-experimental design, cost-effectiveness, water treatment, difference-in-differences, infrastructure evaluation", "contribution statement":