方法对比
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| 逆概率治疗加权法 (IPW / IPTW)× | 因果中介分析（自然直接效应和自然间接效应）× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2000 | 2010 |
| 提出者≠ | Robins, Hernán & Brumback | Pearl (2001); general framework by Imai, Keele & Tingley (2010) |
| 类型≠ | Causal inference weighting estimator | Counterfactual causal decomposition |
| 开创性文献≠ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ | Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗ |
| 别名 | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting | natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation |
| 相关 | 5 | 5 |
| 摘要≠ | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. | Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation. |
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