Application and Comparison of Three Time-Series Causal Discovery Methods in Environmental Epidemiology

Methods

A multi-method causal discovery framework was adopted to perform an empirical analysis of the time-series data of environmental factors and influenza incidence rates across 31 provinces in China. The methodological system included: the PCMCI+ algorithm based on conditional independence tests, the VAR-LiNGAM method based on structural equation models, and the CCM model based on nonlinear state-space reconstruction.Results All three models confirmed that mean temperature exerted a negative causal effect on influenza incidence rates with a one-week lag (p < 0.05), with relative causal strengths of -0.019, -0.047, and -0.078, respectively.