Power-distribution networks in many developing nations face persistent reliability challenges, yet diagnostic methodologies often rely on aggregated, non-experimental data, limiting causal inference on equipment performance. This paper details the design and implementation of a novel randomised field trial (RFT) to causally evaluate the reliability of specific distribution equipment—namely pole-mounted transformers and associated switchgear—under operational conditions. A stratified randomised controlled trial was deployed across multiple urban and peri-urban networks. Equipment units were randomly assigned to a diagnostic intervention group or a control group. Reliability was measured via failure rates and mean time between failures (MTBF) over the trial period. The primary analysis employs a Cox proportional hazards model: $h(t|X) = h_0(t) \exp(\beta_1 \text{Intervention} + \beta_2 \text{Stratum})$, with robust standard errors to account for network clustering. Preliminary analysis indicates a statistically significant reduction in failure hazard for equipment in the intervention group. The estimated hazard ratio is 0.62 (95% CI: 0.51 to 0.75), suggesting a 38% lower risk of failure during the observation period. The RFT framework proves operationally feasible for in-situ reliability diagnostics and yields robust, causal evidence on equipment performance, a marked improvement over observational studies. Utilities should adopt randomised trial designs for piloting new equipment and maintenance protocols. Future work should integrate cost-benefit analysis and expand to rural networks. randomised field trial, distribution reliability, causal inference, power infrastructure, maintenance diagnostics This work provides the first application of a fully randomised controlled trial methodology for directly measuring the causal impact of diagnostic interventions on power-distribution equipment reliability in a sub-Saharan African context.