Abstract: The core challenge in estimating individual causal effects (ICE) from observational data lies in confounding bias and the unobservability of counterfactual outcomes. To eliminate confounding bias, existing methods typically balance all covariates as potential confounders. However, erroneously balancing instrumental variables and adjustment variables introduces estimation bias and increases variance. Although recent studies address this via covariate decomposition to separate confounders, their sample re-weighting approach for balance can lead to extreme weights while neglecting the sample imbalance between treatment and control groups, resulting in inaccurate ICE estimates. To address these limitations, this paper proposes a Individual Causal Effect Estimation method based on Adversarial Learning, which optimizes representation learning and distribution balancing via an adversarial mechanism. Specifically: (i) Sample Processing: The SMOTE algorithm preprocesses data to mitigate sample imbalance between treatment and control groups; (ii) Representation Learning: Two adversarial probability constraint modules are designed to achieve confounder separation via variable disentanglement; (iii) Confounder Balancing: A feature balancer and an adversarial discriminator dynamically align the spatial distributions of confounders across treatment and control groups, constructing an Adversarial Balancing Network to avoid the extreme weight problem inherent in sample re-weighting. Experimental results demonstrate that our algorithm can precisely decompose confounders and significantly improve the accuracy of ICE estimation.