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Causal effect estimation from observational data is a crucial but challengingtask. Currently, only a limited number of data-driven causal effect estimationmethods are available. These methods either provide only a bound estimation ofthe causal effect of a treatment on the outcome, or generate a uniqueestimation of the causal effect, but making strong assumptions on data andhaving low efficiency. In this paper, we identify a practical problem settingand propose an approach to achieving unique and unbiased estimation of causaleffects from data with hidden variables. For the approach, we have developedthe theorems to support the discovery of the proper covariate sets forconfounding adjustment (adjustment sets). Based on the theorems, two algorithmsare proposed for finding the proper adjustment sets from data with hiddenvariables to obtain unbiased and unique causal effect estimation. Experimentswith synthetic datasets generated using five benchmark Bayesian networks andfour real-world datasets have demonstrated the efficiency and effectiveness ofthe proposed algorithms, indicating the practicability of the identifiedproblem setting and the potential of the proposed approach in real-worldapplications.

Towards unique and unbiased causal effect estimation from data with hidden variables