  To determine causal relationships between two variables, approaches based on
Functional Causal Models (FCMs) have been proposed by properly restricting
model classes; however, the performance is sensitive to the model assumptions,
which makes it difficult to use. In this paper, we provide a novel
dynamical-system view of FCMs and propose a new framework for identifying
causal direction in the bivariate case. We first show the connection between
FCMs and optimal transport, and then study optimal transport under the
constraints of FCMs. Furthermore, by exploiting the dynamical interpretation of
optimal transport under the FCM constraints, we determine the corresponding
underlying dynamical process of the static cause-effect pair data. It provides
a new dimension for describing static causal discovery tasks while enjoying
more freedom for modeling the quantitative causal influences. In particular, we
show that Additive Noise Models (ANMs) correspond to volume-preserving
pressureless flows. Consequently, based on their velocity field divergence, we
introduce a criterion for determining causal direction. With this criterion, we
propose a novel optimal transport-based algorithm for ANMs which is robust to
the choice of models and extend it to post-nonlinear models. Our method
demonstrated state-of-the-art results on both synthetic and causal discovery
benchmark datasets.
