  Causal effect estimation from observational data is a crucial but challenging
task. Currently, only a limited number of data-driven causal effect estimation
methods are available. These methods either provide only a bound estimation of
the causal effect of a treatment on the outcome, or generate a unique
estimation of the causal effect, but making strong assumptions on data and
having low efficiency. In this paper, we identify a practical problem setting
and propose an approach to achieving unique and unbiased estimation of causal
effects from data with hidden variables. For the approach, we have developed
the theorems to support the discovery of the proper covariate sets for
confounding adjustment (adjustment sets). Based on the theorems, two algorithms
are proposed for finding the proper adjustment sets from data with hidden
variables to obtain unbiased and unique causal effect estimation. Experiments
with synthetic datasets generated using five benchmark Bayesian networks and
four real-world datasets have demonstrated the efficiency and effectiveness of
the proposed algorithms, indicating the practicability of the identified
problem setting and the potential of the proposed approach in real-world
applications.
