  This paper describes an efficient EM algorithm for maximum likelihood
estimation of a system of nonlinear structural equations corresponding to a
directed acyclic graph model that can contain an arbitrary number of latent
variables. The endogenous variables in the model must be categorical, while the
exogenous variables may be arbitrary. The models discussed in this paper are an
extended version of finite mixture models suitable for causal inference. An
application to the problem of education transmission is presented as an
illustration.
