Abstract: Quantile g-computation is a causal inference method integrating weighted quantile, regression concepts, and g-computation algorithms. This approach evaluates the health effects of mixed exposures by quantile transformation of exposure variables, fitting linear models, and constructing joint marginal structural models. This method is widely applied in fields such as epidemiology and environmental health sciences. It can quantify the overall effect of mixed exposures and analyze the directional weights of individual exposures in the joint effect. Quantile g-computation has the advantages of high computational efficiency and the ability to handle complex nonlinear relationships. By leveraging the "qgcomp" package in R software, it reveals the complex impacts of multiple exposure factors on outcome variables in real-world data applications. This paper systematically introduces the Quantile g-computation model, presents R code implementations, and interprets result based on empirical cases in epidemiological and environmental health research.