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Two-point time-series data, characterized by baseline and follow-upobservations, are frequently encountered in health research. We study a noveltwo-point time series structure without a control group, which is driven by anobservational routine clinical dataset collected to monitor key risk markers oftype-$2$ diabetes (T2D) and cardiovascular disease (CVD). We propose aresampling approach called 'I-Rand' for independently sampling one of the twotime points for each individual and making inference on the estimated causaleffects based on matching methods. The proposed method is illustrated with datafrom a service-based dietary intervention to promote a low-carbohydrate diet(LCD), designed to impact risk of T2D and CVD. Baseline data contain apre-intervention health record of study participants, and health data after LCDintervention are recorded at the follow-up visit, providing a two-pointtime-series pattern without a parallel control group. Using this approach wefind that obesity is a significant risk factor of T2D and CVD, and an LCDapproach can significantly mitigate the risks of T2D and CVD. We provide codethat implements our method.

A resampling approach for causal inference on novel two-point  time-series with application to identify risk factors for type-2 diabetes and  cardiovascular disease