Microbiome associating findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. In this manuscript, we propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM), consisting of linear log-contrast regression and Dirichlet regression, to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels, in the typical three-factor (treatment, microbiome and outcome) causal study design. Two hypothesis tests on the overall mediation effect are proposed as well. Extensive simulated scenarios and real data application show that SparseMCMM has excellent performance in estimation and hypothesis testing. With SparseMCMM, we can get a clear and sensible causal path among treatment, high-dimensional and compositional microbiome and outcome.