  Causal structure learning from observational data remains a non-trivial task
due to various factors such as finite sampling, unobserved confounding factors,
and measurement errors. Constraint-based and score-based methods tend to suffer
from high computational complexity due to the combinatorial nature of
estimating the directed acyclic graph (DAG). Motivated by the `Cause-Effect
Pair' NIPS 2013 Workshop on Causality Challenge, in this paper, we take a
different approach and generate a probability distribution over all possible
graphs informed by the cause-effect pair features proposed in response to the
workshop challenge. The goal of the paper is to propose new methods based on
this probabilistic information and compare their performance with traditional
and state-of-the-art approaches. Our experiments, on both synthetic and real
datasets, show that our proposed methods not only have statistically similar or
better performances than some traditional approaches but also are
computationally faster.
