Causal discovery in mixed additive noise models

Uncovering causal relationships in datasets that include both categorical and continuous variables is a challenging problem. The overwhelming majority of existing methods restrict their application to dealing with a single type of variable. Our contribution is a structural causal model designed to handle mixed-type data through a general function class. We present a theoretical foundation that specifies the conditions under which the directed acyclic graph underlying the causal model can be identified from observed data. In addition, we propose Mixed-type data Extension for Regression and Independence Testing (MERIT), enabling the discovery of causal connections in real-world classification settings. Our empirical studies demonstrate that MERIT outperforms its state-of-the-art competitor in causal discovery on relatively low-dimensional data.