The Multiplicative Quasi-Instrumental Variable Model
乘法准工具变量模型AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究排除限制违反与未测混杂下的准工具变量因果识别和处理效应估计。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
乘法准工具变量模型
- 研究问题
- 当候选工具变量可能直接影响结果、违反标准排除限制时，如何在未测混杂和处理效应异质性下识别已处理者平均效应？
- 方法与思路
- 作者采用准工具变量与隐性混杂之间的乘法治疗模型，通过修正 Wald 比率校正排除限制违反的偏差，并提出多重稳健有效估计量。
- 材料陈述的贡献
- 摘要称框架容许工具的直接效应及相对隐性混杂的处理效应异质性，并以模拟及生育三个或更多孩子对母亲劳动参与的应用评估方法。
- 阅读关注点
- 应核对直接效应允许的具体形式、偏差校正项如何识别、乘法治疗机制的限制，以及异质性容许范围和估计量的稳健性组合。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未完整说明替代识别条件，不能理解为任意无效工具变量均可识别目标效应。
原文摘要
We introduce the Multiplicative Quasi-Instrumental Variable (MQIV) model, a framework for causal inference with unmeasured confounding that leverages an instrument that may be imperfectly exogenous. We allow the candidate quasi-instrument to have a direct effect on the outcome not mediated by the treatment, thus violating the standard IV exclusion restriction. We establish nonparametric identification of the population average treatment effect on the treated (ATT) under a treatment model that is multiplicative with respect to the quasi-IV and the hidden confounder (Hernan and Robins, 2006). Such a multiplicative treatment model may arise naturally either when treatment occurs only if two independent instrument-driven and confounder-driven causal mechanisms are present; or alternatively, when an instrument's effect on treatment uptake is inherently heterogeneous and scales with a person's latent propensity, best capturing settings in which it is challenging for a given instrument to overcome a person's inherent lack of preference for the treatment in view. Importantly, as we establish, the MQIV model is simultaneously agnostic to treatment-effect heterogeneity with respect to hidden confounders and violation of the core IV exclusion restriction condition. Identification is achieved via a modified Wald ratio estimand, which corrects the bias due to the exclusion restriction violation, and we propose a new class of estimators that are multiply robust and semiparametric efficient. Finally, we evaluate the approach in extensive simulations and an application to evaluate the causal effect of having three or more children on mothers' labor-market engagement.