Partial identification of principal causal effects under violations of principal ignorability
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主层可忽略性违反时主层因果效应的部分识别
- 研究问题
- 放松主层可忽略性后，联合建模结局与主层能否点识别因果效应，主层关联参数又在什么条件下能够识别？
- 方法与思路
- 作者分析参数化联合模型的识别边界，讨论较弱假设形成的部分识别区域，并将结论扩展至半参数及非参数贝叶斯模型。
- 材料陈述的贡献
- 摘要称即便主层联合分布已知且简单结局模型正确设定，效应仍可能仅部分识别；关联参数识别还依赖可忽略性违反及强参数约束。
- 阅读关注点
- 应核对部分识别反例的模型设置、替代假设如何收紧识别区域，以及关联参数识别为何要求违反可忽略性和强参数约束。
- 阅读边界
- 仅据 arXiv 预印本摘要，不能确认同行评审或期刊接收；具体识别区域、证明条件及贝叶斯扩展的适用范围均未说明。
原文摘要
Principal stratification is a general framework for studying causal mechanisms involving post-treatment variables. When estimating principal causal effects, the principal ignorability assumption is commonly invoked, which we study in detail in this manuscript. Our first key contribution is studying a commonly used strategy of using parametric models to jointly model the outcome and principal strata without requiring the principal ignorability assumption. We show that even if the joint distribution of principal strata is known, this strategy necessarily leads to only partial identification of causal effects, even under very simple and correctly specified outcome models. While principal ignorability leads to point identification in this setting, we discuss alternative, weaker assumptions and show how they can lead to informative partial identification regions. An additional contribution is that we provide theoretical support to strategies used in the literature for identifying association parameters that govern the joint distribution of principal strata. We prove that this is possible, but only if the principal ignorability assumption is violated. Additionally, due to partial identifiability of causal effects even when these association parameters are known, we show that these association parameters are only identifiable under strong parametric constraints. Lastly, we extend these results to more flexible semiparametric and nonparametric Bayesian models.