In this paper, we discuss identifiability of mediation, direct and indirect effects of treatment on outcome. The mediation effects are represented by a causal mediation model which includes an unobserved confounder (i.e., a common cause of the mediator and the outcome variable), and the direct and indirect effects are represented by the mediation effects. Without requiring the sequential ignorability assumption or the exclusion restriction assumption (i.e., the absence of direct effect of treatment on outcome), we require that only treatment is randomized and that the degree of equation nonlinearity for the treatment effect on the mediator is higher than that for the outcome. If the requirement of nonlinearity degree is not satisfied, we may use a covariate as an instrumental variable to improve the identifiability. In this paper, we focus on the identifiability of parameters, although, to illustrate our identifiability results, we describe estimation approaches. The simulations show good estimation performance by our approach compared to the standard mediation approach.