This document discusses causal inference methods for uncertain time series data. It presents a method for analyzing time-stamped medical sensor data to determine causal relationships between variables like glucose levels, exercise, sleep, and insulin dosage. The method is applied to data from 17 subjects with Type 1 diabetes over 72 hours. The results suggest that very vigorous exercise leads to short-term hyperglycemia, which is supported by literature. Open problems discussed include handling latent variables, non-stationary data, real-time prediction and explanation, and simulation.