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Observational studies have recently received significant attention from themachine learning community due to the increasingly available non-experimentalobservational data and the limitations of the experimental studies, such asconsiderable cost, impracticality, small and less representative sample sizes,etc. In observational studies, de-confounding is a fundamental problem ofindividualised treatment effects (ITE) estimation. This paper proposesdisentangled representations with adversarial training to selectively balancethe confounders in the binary treatment setting for the ITE estimation. Theadversarial training of treatment policy selectively encouragestreatment-agnostic balanced representations for the confounders and helps toestimate the ITE in the observational studies via counterfactual inference.Empirical results on synthetic and real-world datasets, with varying degrees ofconfounding, prove that our proposed approach improves the state-of-the-artmethods in achieving lower error in the ITE estimation.

Adversarial De-confounding in Individualised Treatment Effects Estimation