In this paper we develop self-consistent and smoothed dependent estimators for the cause-specific failure time density in a competing risks context, employed in the presence of both left-censored and right-censored data, while allowing for masking of the failure cause. Dependence will be incorporated between the failure times and both the censoring times and the masked causes with the use of both Kernel Regression and Multivariate Multiple Regression at each iteration of the algorithm. Our approach to modeling the cause-specific failure times is intended to be the most automated and data-driven approach possible.