"background": "Evaluating the real-world adoption of engineered water treatment systems is critical for assessing their public health impact and sustainability. Current assessments often rely on self-reported data, which can be unreliable and lack causal inference for factors influencing uptake. ", "purpose and objectives": "This short report presents a novel methodological framework for measuring household adoption rates of point-of-use water treatment technologies. The primary objective is to demonstrate a quasi-experimental design that isolates the causal effect of system deployment from confounding variables. ", "methodology": "A quasi-experimental, pre-post intervention design with a non-equivalent control group was implemented across multiple rural communities. Household adoption was measured via direct observation and residual chlorine testing. The treatment effect was estimated using a difference-in-differences model: Y{it = \0 + \1 + \2 + \3 (\) +, with robust standard errors clustered at the community level. ", "findings": "The methodological application yielded a precise estimate of the causal adoption rate, which was 34 percentage points higher in intervention communities compared to the control group (95% CI: 28 to 40). The framework successfully identified technical maintenance access as a primary moderator of sustained use. ", "conclusion": "The proposed quasi-experimental design provides a rigorous, evidence-based methodology for evaluating the functional uptake of engineered water treatment systems, moving beyond mere installation metrics. ", "recommendations": "Future engineering evaluations should incorporate controlled observational designs and direct measurement to generate reliable adoption data. This approach should be integrated into the post-deployment monitoring phase of water infrastructure projects. ", "key words": "water treatment adoption, quasi-experimental design, difference-in-differences, monitoring and evaluation, causal inference", "contribution statement": "This paper provides a novel methodological framework that enables causal estimation of technology adoption rates, directly addressing the
Merwe et al. (Tue,) studied this question.