In micro-lens-array-based light-field imaging, the micro-lens centers serve as the origins of local micro-lens coordinate systems. Each micro-lens receives angular/depth information coded according to its center location. Therefore, the errors in positioning the micro-lens centers will lead to errors in depth estimation. This paper proposes a method that resolves error propagation from raw light-field data to depth estimation based on analyzing large amounts of simulated images with various aperture sizes, noise levels, and object distance values. The simulation employs backward ray tracing and Monte Carlo sampling to improve computational efficiency. The errors are counted and accumulated stepwise from center positioning and generation of sub-aperture images to depth estimation. The disparity errors calculated during depth estimation are shown to be more apparent either with more significant center positioning errors or with a greater defocusing distance. An experiment using an industrial light-field camera is conducted, confirming that disparity errors at considerable object distances can be reduced significantly when the micro-lens centers are positioned with higher accuracy.