Nonparametric Instrumental Variable Inference with Many Weak Instruments
多弱工具变量下的非参数工具变量推断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：摘要研究工具变量下的非参数结构函数估计，并以长期因果效应为动机，对应结构估计与因果推断。
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…
多弱工具变量下的非参数工具变量推断
- 研究问题
- 当离散工具变量的取值数随样本量增长、每个取值对应的样本有限时，如何对非参数结构函数的线性泛函进行有意义的推断？
- 方法与思路
- 作者以矩约束的最小范数解及其泛函构造渐近目标，将 JIVE 扩展为 npJIVE，并建立自动去偏机器学习估计与弱识别效率理论。
- 材料陈述的贡献
- 摘要称该方法以渐近逼近替代通常失败的点识别，并为结构函数及其最小范数投影的泛函提供多弱工具变量下的有效率估计。
- 阅读关注点
- 应核对最小范数解所用空间与范数、逼近目标成立的条件，以及 npJIVE 的正则化和去偏步骤，区分投影泛函与原结构泛函。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未说明工具变量强度、维度增长和逼近误差的具体条件，效率结论需结合全文。
原文摘要
We study inference on linear functionals in the nonparametric instrumental variable (NPIV) problem with a discretely-valued instrument under a many-weak-instruments asymptotic regime, where the number of instrument values grows with the sample size. A key motivating example is estimating long-term causal effects in a new experiment with only short-term outcomes, using past experiments to instrument for the effect of short- on long-term outcomes. Here, the assignment to a past experiment serves as the instrument: we have many past experiments but only a limited number of units in each. Since the structural function is nonparametric but constrained by only finitely many moment restrictions, point identification typically fails. To address this, we consider linear functionals of the minimum-norm solution to the moment restrictions, which is always well-defined. As the number of instrument levels grows, these functionals define an approximating sequence to a target functional, replacing point identification with a weaker asymptotic notion suited to discrete instruments. Extending the Jackknife Instrumental Variable Estimator (JIVE) beyond the classical parametric setting, we propose npJIVE, a nonparametric estimator for solutions to linear inverse problems with many weak instruments. We construct automatic debiased machine learning estimators for linear functionals of both the structural function and its minimum-norm projection, and establish their efficiency in the many-weak-instruments regime. To do so, we develop a general semiparametric efficiency theory for regular estimators under weak identification and many-weak-instrument asymptotics.