Deep reinforcement learning has achieved significant breakthroughs in various fields, with policy gradient algorithms widely adopted due to their suitability for handling nonlinear and high-dimensional state spaces. However, in practical applications, existing policy gradient algorithms still suffer from high variance, which slows convergence and may cause suboptimal solutions. To tackle this challenge, a variance optimization method for policy gradients is proposed from a latent variable causal model perspective. By introducing latent variables to characterize unobserved random information, a latent variable causal model is constructed and learned. Utilizing this model, a causal value function is proposed and combined with long short-term memory (LSTM) networks to differentiate the temporal impact of unobserved information on value estimation. This approach improves the accuracy of action advantage function estimation and reduces policy gradient variance. Experiments demonstrate that the proposed latent variable causal model outperforms state-of-the-art algorithms across multiple tasks, with better performance and stability.