  We focus on the extension of bivariate causal learning methods into
multivariate problem settings in a systematic manner via a novel framework. It
is purposive to augment the scale to which bivariate causal discovery
approaches can be applied since contrast to traditional causal discovery
methods, bivariate methods render estimation in the form of a causal Directed
Acyclic Graph (DAG) instead of its complete partial directed acyclic graphs
(CPDAGs). To tackle the problem, an auxiliary framework is proposed in this
work so that together with any bivariate causal inference method, one could
identify and estimate causal structure over variables more than two from
observational data. In particular, we propose a local graphical structure in
causal graph that is identifiable by a given bivariate method, which could be
iteratively exploited to discover the whole causal structure under certain
assumptions. We show both theoretically and experimentally that the proposed
framework can achieve sound results in causal learning problems.
