Quasi Instrumental Variable Methods for Stable Hidden Confounding and Binary Outcome
稳定隐性混杂与二元结果的准工具变量方法AI 解读 ↓
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稳定隐性混杂与二元结果的准工具变量方法
- 研究问题
- 在传统工具变量的排除限制或独立性难以满足、结果又为二元变量时，能否利用准工具变量和稳定混杂结构检验因果零假设并识别 ATT？
- 方法与思路
- 作者用结构均衡动态生成模型论证乘法尺度混杂稳定性与跨准工具变量的 ATT 稳定性，并提出广义优势乘积重参数化及两类估计方法。
- 材料陈述的贡献
- 摘要称混杂稳定性可支持因果零假设检验，两项稳定性共同支持条件和边际 ATT 识别；提出极大似然及三重稳健、局部有效的估计量。
- 阅读关注点
- 核对动态模型如何推出两项稳定性、准工具变量预测性为何足够参与该框架，以及二元结果的边界约束和三重稳健对应的模型组合。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未提供稳定性的详细条件，不能仅凭某变量预测结果就宣称 ATT 已获识别。
原文摘要
Instrumental variable (IV) methods are central to causal inference from observational data, particularly when a randomized experiment is not feasible. However, of the three conventional core IV identification conditions, only one, IV relevance, is empirically verifiable; often one or both of the other conditions, exclusion restriction and IV independence from unmeasured confounders, are unmet in real-world applications. These challenges are compounded when the outcome is binary, a setting for which robust IV methods remain underdeveloped. A fundamental contribution of this paper is the development of a general identification strategy justified under a structural equilibrium dynamic generative model of so-called stable confounding and a quasi instrumental variable (QIV), i.e. a variable that is only assumed to be predictive of the outcome. Such a model implies (a) stability of confounding on the multiplicative scale, and (b) stability of the additive average treatment effect among the treated (ATT), across levels of that QIV. The former is all that is necessary to ensure a valid test of the causal null hypothesis; together those two conditions establish nonparametric identification and estimation of the conditional and marginal ATT. To address the statistical challenges posed by the need for boundedness in binary outcomes, we introduce a generalized odds product re-parametrization of the observed data distribution, and we develop both a principled maximum likelihood estimator and a triply robust semiparametric locally efficient estimator, which we evaluate through simulations and an empirical application to the UK Biobank.