  Identifying causal relations is crucial for a variety of downstream tasks. In
additional to observational data, background knowledge (BK), which could be
attained from human expertise or experiments, is usually introduced for
uncovering causal relations. This raises an open problem that in the presence
of latent variables, what causal relations are identifiable from observational
data and BK. In this paper, we propose two novel rules for incorporating BK,
which offer a new perspective to the open problem. In addition, we show that
these rules are applicable in some typical causality tasks, such as determining
the set of possible causal effects with observational data. Our rule-based
approach enhances the state-of-the-art method by circumventing a process of
enumerating block sets that would otherwise take exponential complexity.
