Identifying factors and mechanisms that influence survival time of cancer patients is critical for healthcare decision makers. Besides the medical conditions, socioeconomic status (SES) of a patient may also significantly affect the prognosis of the disease. This study aims to investigate key determinants that affect lung cancer survival and to evaluate the direct and indirect effects (via other mediating variables) of SES on survival time. Bayesian Networks (BNs) were proposed and implemented to analyze a large database from The Surveillance, Epidemiology, and End Results (SEER) of the National Cancer Institute of the United States. Results show that the cancer stage at diagnosis is the most critical factor for determining survival time. Investigation of the underlying mechanism identifies both direct and indirect effects of SES on survival time, but the mediation analysis also indicates that the disparity on timely diagnosis (i.e., stage at diagnosis) caused by SES is only marginally significant.