Causal inference in the field of infectious disease attempts to gain insight into the potential causal nature of an association between risk factors and diseases. Simulated causality inference experiments have shown preliminary promise in improving understanding of the transmission of infectious diseases but still lack sufficient quantitative causal inference studies based on real-world data. Here, we investigate the causal interactions between three different infectious diseases and related factors, using causal decomposition analysis, to characterize the nature of infectious disease transmission. We show that the complex interactions between infectious disease and human behavior have a quantifiable impact on transmission efficiency of infectious diseases. Our findings, by shedding light on the underlying transmission mechanism of infectious diseases, suggest that causal inference analysis is a promising approach to determine epidemiological interventions.