Learning Causal Structure on Mixed Data with Tree-Structured Functional Models

A general tree-structured functional causal model is introduced, well suited for characterizing the generating mechanisms of mixed data by allowing non-differentiability and nonlinearity and it is proved that the causal direction between continuous and discrete variables is generally identifiable under a much larger function class.