Counterfactual prediction and selection bias are major challenges in causal effect estimation. To effectively represent the complex mixed distribution of potential covariant and enhance the generalization ability of counterfactual prediction， a Re-weighted adversarial Variational AutoEncoder Network （RVAENet） model was proposed for industrial causal effect estimation. To address bias problem in mixed distribution， the idea of domain adaptation was adopted， and an adversarial learning mechanism was used to balance the representation learning distribution of the latent variables obtained by the Variational AutoEncoder （VAE）. Furthermore， the sample propensity weights were learned to re-weight the samples， reducing the distribution difference between the treatment group and the control group. The experimental results show that， in two scenarios of the industrial real-world datasets， the Areas Under Uplift Curve （AUUC） of the proposed model are improved by 15.02% and 16.02% compared to TEDVAE （Treatment Effect with Disentangled VAE）. On the public datasets， the proposed model generally achieves optimal results for Average Treatment Effect （ATE） and Precision in Estimation of Heterogeneous Effect （PEHE）.