In this paper, we consider analysis of a competing risks model using binomial removal based on progressive censored data when failure modes are only partially observed. The latent lifetimes of competing risks follow inverted exponentiated exponential distributions with different shape parameters and a common scale parameter. We explore the study by estimating all unknown parameters using classical and Bayesian techniques. First, we obtain maximum likelihood estimates (MLEs) of model parameters. Subsequently, interval estimates are derived based on the observed Fisher information matrix. We obtain Bayesian estimates using squared error and linear exponential (LINEX) loss functions. The highest posterior density (HPD) intervals are also obtained. We examine impact of removal probability p on the expected experiment time (EET) under progressively censored data. We conduct extensive simulation study to evaluate the performance of all estimators. Numerical illustrations are presented from application viewpoints.