  The inaccessibility of controlled randomized trials due to inherent
constraints in many fields of science has been a fundamental issue in causal
inference. In this paper, we focus on distinguishing the cause from effect in
the bivariate setting under limited observational data. Based on recent
developments in meta learning as well as in causal inference, we introduce a
novel generative model that allows distinguishing cause and effect in the small
data setting. Using a learnt task variable that contains distributional
information of each dataset, we propose an end-to-end algorithm that makes use
of similar training datasets at test time. We demonstrate our method on various
synthetic as well as real-world data and show that it is able to maintain high
accuracy in detecting directions across varying dataset sizes.
