  Observational studies have recently received significant attention from the
machine learning community due to the increasingly available non-experimental
observational data and the limitations of the experimental studies, such as
considerable cost, impracticality, small and less representative sample sizes,
etc. In observational studies, de-confounding is a fundamental problem of
individualised treatment effects (ITE) estimation. This paper proposes
disentangled representations with adversarial training to selectively balance
the confounders in the binary treatment setting for the ITE estimation. The
adversarial training of treatment policy selectively encourages
treatment-agnostic balanced representations for the confounders and helps to
estimate the ITE in the observational studies via counterfactual inference.
Empirical results on synthetic and real-world datasets, with varying degrees of
confounding, prove that our proposed approach improves the state-of-the-art
methods in achieving lower error in the ITE estimation.
