Ensemble Doubly Robust Bayesian Inference via Regression Synthesis
通过回归合成开展集成双重稳健贝叶斯推断AI 解读 ↓
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通过回归合成开展集成双重稳健贝叶斯推断
- 研究问题
- 当倾向得分模型与结果模型都可能设定错误时，如何合成多个候选模型，降低平均处理效应估计的偏差并量化不确定性？
- 方法与思路
- 作者提出双重稳健贝叶斯回归合成，分别集成倾向得分与结果模型，以个体层面更新的权重适应异质性，并进行后验计算。
- 材料陈述的贡献
- 摘要称即使没有单个模型正确，所提集成仍可获得一致性；模拟与母亲吸烟对出生体重的应用用于展示其估计表现。
- 阅读关注点
- 应核对集成一致性所需的模型覆盖与先验条件、权重识别、处理效应识别假设，以及后验区间的频率学覆盖表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明一致性的具体条件，不能理解为任意双重误设下均无偏。
原文摘要
The doubly robust estimator, which models both the propensity score and outcomes, is a popular approach to estimate the average treatment effect in the potential outcome setting. The primary appeal of this estimator is its theoretical property, wherein the estimator achieves consistency as long as either the propensity score or outcomes is correctly specified. In most applications, however, both are misspecified, leading to considerable bias that cannot be checked. In this paper, we propose a Bayesian ensemble approach that synthesizes multiple models for both the propensity score and outcomes, which we call doubly robust Bayesian regression synthesis. Our approach applies Bayesian updating to the ensemble model weights that adapt at the unit level, incorporating data heterogeneity, to significantly mitigate misspecification bias. Theoretically, we show that our proposed approach is consistent regarding the estimation of both the propensity score and outcomes, ensuring that the doubly robust estimator is consistent, even if no single model is correctly specified. An efficient algorithm for posterior computation facilitates the characterization of uncertainty regarding the treatment effect. Our proposed approach is compared against standard and state-of-the-art methods through two comprehensive simulation studies, where we find that our approach is superior in all cases. An empirical study on the impact of maternal smoking on birth weight highlights the practical applicability of our proposed method.