Regression modelCausal
G-计算（参数G-公式）
G-计算是一种因果推断方法，用于从观察数据中估计干预或治疗对结果的影响。该方法由James M. Robins于1986年提出，它提供了一种参数化的标准化方法，能够处理时变暴露和混杂因素。通过利用拟合的结果模型，该方法估计在不同干预情景下人群结果会是怎样的。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods: application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512. DOI: 10.1016/0270-0255(86)90088-6 ↗
- Taubman, S. L., Robins, J. M., Mittleman, M. A., & Hernán, M. A. (2009). Intervening on risk factors for coronary heart disease: an application of the parametric g-formula. International Journal of Epidemiology, 38(6), 1599-1611. DOI: 10.1093/ije/dyp192 ↗
- Ahern, J., Hubbard, A., & Galea, S. (2009). Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods. American Journal of Epidemiology, 169(9), 1140-1147. DOI: 10.1093/aje/kwp015 ↗
如何引用本页
ScholarGate. (2026, June 3). G-Computation (Parametric G-formula). ScholarGate. https://scholargate.app/zh/causal-inference/g-computation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →