未测混杂因素和依时混杂因素是影响因果推断准确性的主要因素，目前尚无有效控制其影响的统计模型，使其成为因果推断亟待解决的重要瓶颈问题。本研究提出一种新的因果推断方法——g-computation多元联合混合效应模型：该模型将未测混杂因素作为随机效应进行控制，通过序列相关系数控制依时混杂因素的交叉影响，将结果变量模型、依时暴露因素模型和依时混杂因素模型共享同一随机效应构建多元联合混合效应模型，利用自适应高斯求积法进行参数估计；通过g-computation对模型参数估计值构建因果关系函数，对结果变量条件期望值进行积分运算作出因果推断；最后，将该模型应用于估计绝经期女性FSH综合调控骨量及脂肪能量代谢的因果效应，将理论创新转化为实际价值。因此，本研究拓展纵向研究因果推断方法的同时为骨质疏松和内脏脂肪堆积提供共同预防靶点，具有重要的理论意义和实际应用价值。

At present, unmeasured confounders and time-dependent confounders are the main influence factors of accuracy of causal inference. There are few models that can effectively control the effects of these confounders. As a result, they have become the important bottleneck problems for causal inference in the longitudinal study. In this study, a new causal inference approach was proposed: g-computation multivariate joint mixed-effects model. In this model, unmeasured confounders were controlled as random effects. Series correlation was employed to address the cross impact of time-dependent confounders. Multivariate joint mixed-effects model was constructed with outcome variable model, time-dependent exposure model, and time-dependent confounder model sharing the common random effect. Adaptive Gauss-Hermite quadrature was used to estimate the parameters of model. Then, g-computation was used to construct the causal function of estimators, which could infer the causal effect by integrating outcome expected values over covariate histories. Lastly, this model was applied to the longitudinal data of follicle-stimulating hormone commonly regulating and controlling the bone mass and fat energy metabolism in postmenopausal women. The causal effect of follicle-stimulating hormone was approximatively unbiasedly estimated. Therefore, the theoretical innovation would be translated into actual value. In conclusion, this study could not only extend the causal inference approaches in the longitudinal study, but also provide the common prevention target of osteoporosis and visceral fat accumulation. Therefore, the results of this study were very significant for theoretical research and valuable for practical application.