# 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

## TL;DR

This paper introduces a new resampling method to infer causal effects from two-point time-series data, applied to study risk factors for type-2 diabetes and cardiovascular disease.

## Contribution

The novel 'I-Rand' resampling approach enables causal inference in two-point time-series without a control group.

## Key findings

- Obesity is identified as a significant risk factor for type-2 diabetes and cardiovascular disease.
- A low-carbohydrate diet significantly reduces the risk of type-2 diabetes and cardiovascular disease.

## Abstract

Two-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel two-point time-series structure without a control group, which is driven by an observational routine clinical dataset collected to monitor key risk markers of type-2 diabetes (T2D) and cardiovascular disease (CVD). We propose a resampling approach called “I-Rand” for independently sampling one of the two-time points for each individual and making inferences on the estimated causal effects based on matching methods. The proposed method is illustrated with data from a service-based dietary intervention to promote a low-carbohydrate diet (LCD), designed to impact risk of T2D and CVD. Baseline data contain a pre-intervention health record of study participants, and health data after LCD intervention are recorded at the follow-up visit, providing a two-point time-series pattern without a parallel control group. Using this approach we find that obesity is a significant risk factor of T2D and CVD, and an LCD approach can significantly mitigate the risks of T2D and CVD. We provide code that implements our method.