In causal inference, non-parametric methods do not restrict causal mechanisms to a specific class of functions $\mathcal{F}$. This flexibility allows them to better model complex, real-world data where the interaction between cause and noise may be non-linear or non-additive. While these methods often yield better empirical results than restricted-class approaches, the lack of explicit functional restrictions leads to a loss of theoretical identifiability guarantees. To recover causal direction, non-parametric methods typically rely on setting a smooth prior on the complexity of the causal mechanisms.