Calibrated Bayesian Inference for Stochastic Intervention Effects
随机干预效应的校准贝叶斯推断AI 解读 ↓
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随机干预效应的校准贝叶斯推断
- 研究问题
- 灵活非参数贝叶斯模型对随机干预效应给出的后验不确定性，如何在不改变先验与拟合算法的情况下获得频率学覆盖校准？
- 方法与思路
- 作者对后验样本进行后处理修正，建立半参数 Bernstein–von Mises 理论，覆盖多类随机干预，并发展 SoftBART 的相关条件。
- 材料陈述的贡献
- 摘要称，修正后验可实现渐近有效推断和有效频率覆盖；模拟显示相对未修正分析偏差与覆盖改善，并用于他汀治疗的 LDL 应用。
- 阅读关注点
- 应核对后处理公式、干预是否依赖处理机制、SoftBART 所需条件，以及理论渐近覆盖与模拟有限样本覆盖之间的对应关系。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核验。摘要未列出完整正则条件与模拟数值，不能据此保证任意贝叶斯模型都能有效校准。
原文摘要
Causal inference increasingly extends beyond classical causal effects defined by deterministic treatment assignments, such as the average treatment effect, to stochastic intervention effects that can weaken positivity requirements and offer greater policy relevance. Nonparametric Bayesian models are attractive for estimating these effects due to their flexibility and inherent uncertainty propagation, but this posterior uncertainty need not be well calibrated for the causal effect of interest. We develop a simple post-processing correction that can be applied to posterior samples without changing the prior or fitting algorithm. We prove that, for a broad class of stochastic interventions, the corrected posterior yields asymptotically efficient inference and credible intervals with asymptotically valid frequentist coverage; formally, it satisfies a semiparametric Bernstein-von Mises theorem. The theory covers interventions specified independently of the observed treatment process, as well as interventions that modify it, including incremental propensity score interventions and a new power-tilt intervention. A central contribution is new theory for SoftBART, including conditions under which this flexible tree-based Bayesian model supports calibrated Bayesian inference for stochastic intervention effects. In simulations, the correction reduces bias and improves coverage relative to the uncorrected Bayesian analysis while remaining competitive with frequentist alternatives. We illustrate the method by estimating how expected LDL cholesterol would change under hypothetical increases or decreases in the odds of receiving statin therapy.