方法对比
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| 贝叶斯边际结构模型× | 贝叶斯双重差分法× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2015 (Bayesian extension); 2000 (MSM foundation) | 2015-2023 |
| 提出者≠ | Saarela, Stephens, Moodie & Klein (Bayesian extension); Robins, Hernan & Brumback (original MSM) | Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series) |
| 类型≠ | Causal inference / Bayesian weighted regression | Bayesian causal inference / panel regression |
| 开创性文献≠ | Saarela, O., Stephens, D. A., Moodie, E. E. M., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288. DOI ↗ | Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529. link ↗ |
| 别名 | Bayesian MSM, Bayesian MSM-IPW, Bayesian weighted structural model, Bayesian causal MSM | Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator |
| 相关≠ | 6 | 5 |
| 摘要≠ | Bayesian Marginal Structural Model (Bayesian MSM) combines the causal identification power of inverse-probability-weighted marginal structural models with Bayesian posterior inference. Rather than relying on point estimates and asymptotic standard errors, it propagates uncertainty through a full posterior distribution over causal effect parameters, offering coherent uncertainty quantification for causal effects of time-varying treatments. | Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available. |
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