<p>Traditional causal inference methods face challenges when dealing with causal effect estimation in the presence of missing data. This paper proposes the Missing Imputation Generative Adversarial Network Average Treatment Effect (MIGANATE) model which provides a possible solution for this problem. MIGANATE estimates average treatment effects (ATE) with missing data imputation using Generative Adversarial Networks. This model consists of two sub-models. The first sub-model, Missing Imputation Generative Adversarial Network (MIGAN), uses adversarial learning between a generator and a discriminator to generate imputed values that approximate the distribution of the true data, thereby addressing the missing data issue and providing complete data for subsequent ATE estimation. The second sub-model, GANATE, comprises a counterfactual module and a treatment effect module. The counterfactual generator produces counterfactual outcomes, which are then passed to the treatment effect module, where the Generative Adversarial Networks (GAN) is trained by minimizing an empirical loss function to estimate the ATE. A numerical study compares the effectiveness of the MIGANATE model with the Inverse Probability of Treatment Weighting (IPTW) and Covariate Balancing Propensity Score (CBPS) methods in estimating ATE. The results show that MIGANATE can provide more stable and accurate causal effect estimates in the presence of missing data. In addition, the effectiveness of the proposed method is illustrated by a real data example.</p>