  We explore the usage of meta-learning to derive the causal direction between
variables by optimizing over a measure of distribution simplicity. We
incorporate a stochastic graph representation which includes latent variables
and allows for more generalizability and graph structure expression. Our model
is able to learn causal direction indicators for complex graph structures
despite effects of latent confounders. Further, we explore robustness of our
method with respect to violations of our distributional assumptions and data
scarcity. Our model is particularly robust to modest data scarcity, but is less
robust to distributional changes. By interpreting the model predictions as
stochastic events, we propose a simple ensemble method classifier to reduce the
outcome variability as an average of biased events. This methodology
demonstrates ability to infer the existence as well as the direction of a
causal relationship between data distributions.
