Previous causal discovery approaches, which allow latent confounders and causal relationships among those latent variables, either assume linear causal relationships or assume discrete data. Representative approaches along this line include rank deficiency-based methods, matrix decomposition-based methods, generalized independent noise condition-based methods, and mixture oracles-based methods. (1) Rank deficiency-based. By testing the rank deficiency over cross-covariance matrices over observed variables, one is able to locate latent variables and identify the causal relationships among them in linear-Gaussian models. Silva et al. [2006a], Kummerfeld and Ramsey [2016] make use of the Tetrad condition, a special case of the rank-deficiency constraints, to handle the case where each observed variable is influenced by only one latent parent, and each latent variable has at least three pure measured children. The Tetrad condition has also been used to identify a latent tree structure [Pearl, 1988]. Recently, the rank-deficiency constraints have been extended to identify more general hierarchical structures [Huang et al., 2022]. (2) Matrix decomposition-based. It has been shown that, under certain conditions, the precision matrix can be decomposed into a low-rank matrix and a sparse matrix, where the low-rank matrix represents the causal structure from latent variables to observed variables and the sparse matrix gives the structural relationships over observed variables. To achieve such decomposition, certain assumptions are imposed on the structure [Chandrasekaran et al., 2011, 2012, Anandkumar et al., 2013], e.g., there should be three times more measured variables than latent variables. (3) Generalized independent noise (GIN) condition-based. The GIN condition is an extension of the independent noise condition in the existence of latent confounders, relying on higher-order statistics to identify latent structures. In particular, Xie et al. [2020] proposes a GIN-based approach that allows multiple latent parents behind every pair of observed variables and can identify causal directions among latent variables. Moreover, Adams et al. [2021] gives necessary and sufficient structural constraints in the linear, non-Gaussian or heterogeneous case, to identify the latent structures. (4) Mixture oracles-based method-based. Recently, Kivva et al. [2021] proposed a mixture oracles-based method to identify the latent variable graph that allows nonlinear causal relationships. However, it requires that the latent variables are discrete and each latent variable has measured variables as children. Thanks to the discreteness assumption, it can handle general DAGs over latent variables. On the other hand, regarding the scenario of latent hierarchical structures, most previous work along this line assumes a tree structure and requires that each variable has only one dimension and that the data is either linear-Gaussian or discrete [Pearl, 1988, Zhang, 2004, Choi et al., 2011, Drton et al., 2017, Huang et al., 2020]. In contrast, we address the general nonlinear case with continuous variables. Moreover, our conditions allow for multiple undirected paths between two variables and thus are more general than tree-based assumptions in prior work.