{ "background": "District hospitals are critical nodes in Ethiopia's healthcare system, yet persistent operational inefficiencies undermine service delivery. While quasi-experimental designs are increasingly employed to evaluate interventions aimed at improving efficiency, a systematic assessment of their methodological rigour and application in this specific context is lacking.", "purpose and objectives": "This systematic review aims to critically appraise the application of quasi-experimental methodologies in studies measuring operational efficiency gains within Ethiopian district hospitals, evaluating their design validity, analytical approaches, and the robustness of causal inferences drawn.", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Eligible studies were those employing quasi-experimental designs to assess operational efficiency outcomes in Ethiopian district hospitals. Studies were screened, selected, and their methodological quality assessed using a modified version of the ROBINS-I tool. Data were extracted on design, analytical techniques, and key findings. A common analytical model was the difference-in-differences specification: $Y{it} = \\beta0 + \\beta1 \\text{Treat}i + \\beta2 \\text{Post}t + \\beta3 (\\text{Treat}i \\times \\text{Post}t) + \\epsilon{it}$, where inference relied on cluster-robust standard errors.", "findings": "Of the 27 included studies, a predominant theme was the reliance on uncontrolled before-and-after designs, which constituted 63% of the reviewed literature. These studies frequently reported positive efficiency gains from interventions like the Lean management system, but the causal estimates were often compromised by significant confounding bias and insufficient adjustment for secular trends. Only a minority of studies employed more robust designs such as interrupted time series or regression discontinuity.", "conclusion": "The evidence base is characterised by a preponderance of methodologically weak quasi-experimental designs, which limits the reliability of causal claims about efficiency-enhancing interventions. There is a pressing need for more sophisticated evaluation frameworks to generate credible evidence for policy.", "recommendations": "Future