  Causality is important for designing interpretable and robust methods in
artificial intelligence research. We propose a local approach to identify
whether a variable is a cause of a given target based on causal graphical
models of directed acyclic graphs (DAGs). In general, the causal relation
between two variables may not be identifiable from observational data as many
causal DAGs encoding different causal relations are Markov equivalent. In this
paper, we first introduce a sufficient and necessary graphical condition to
check the existence of a causal path from a variable to a target in every
Markov equivalent DAG. Next, we provide local criteria for identifying whether
the variable is a cause/non-cause of the target. Finally, we propose a local
learning algorithm for this causal query via learning local structure of the
variable and some additional statistical independence tests related to the
target. Simulation studies show that our local algorithm is efficient and
effective, compared with other state-of-art methods.
