  Causal effect estimation from observational data is a challenging problem,
especially with high dimensional data and in the presence of unobserved
variables. The available data-driven methods for tackling the problem either
provide an estimation of the bounds of a causal effect (i.e. nonunique
estimation) or have low efficiency. The major hurdle for achieving high
efficiency while trying to obtain unique and unbiased causal effect estimation
is how to find a proper adjustment set for confounding control in a fast way,
given the huge covariate space and considering unobserved variables. In this
paper, we approach the problem as a local search task for finding valid
adjustment sets in data. We establish the theorems to support the local search
for adjustment sets, and we show that unique and unbiased estimation can be
achieved from observational data even when there exist unobserved variables. We
then propose a data-driven algorithm that is fast and consistent under mild
assumptions. We also make use of a frequent pattern mining method to further
speed up the search of minimal adjustment sets for causal effect estimation.
Experiments conducted on extensive synthetic and real-world datasets
demonstrate that the proposed algorithm outperforms the state-of-the-art
criteria/estimators in both accuracy and time-efficiency.
