Partial identification via conditional linear programs: estimation and policy learning
基于条件线性规划的部分识别：估计与政策学习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：摘要以潜在结果联合分布、工具变量和因果政策价值作为应用，直接支持因果推断分类。
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…
关注这篇论文的作者
基于条件线性规划的部分识别：估计与政策学习
- 研究问题
- 当目标参数只受到依赖协变量的线性约束、无法点识别时，如何估计其上下界、开展推断，并支持价值仅被部分识别的政策选择？
- 方法与思路
- 作者将参数界写为条件线性规划解的期望，提出利用求解器输出去偏和采用熵正则化平滑的两类估计器，并扩展到政策学习。
- 材料陈述的贡献
- 摘要称两类估计器在给定条件下渐近正态，对条件目标与约束估计的一阶误差稳健，可构造 Wald 型区间并用于医疗补助研究。
- 阅读关注点
- 应核对线性规划解的稳定性、去偏所用求解器输出、熵正则化误差的控制，以及渐近正态和政策学习扩展的具体条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未列出正则条件、平滑参数选择或应用结果，不能概括为所有部分识别问题均适用。
原文摘要
Many important quantities of interest are only partially identified from observable data: the data can limit them to a set of plausible values, but not uniquely determine them. This paper develops a unified framework for covariate-assisted estimation, inference, and decision making in partial identification problems where the parameter of interest satisfies a series of linear constraints, conditional on covariates. In such settings, bounds on the parameter can be written as expectations of solutions to conditional linear programs that optimize a linear function subject to linear constraints, where both the objective function and the constraints may depend on covariates and need to be estimated from data. Examples include estimands involving the joint distributions of potential outcomes, policy learning with inequality-aware value functions, and instrumental variable settings. We propose two de-biased estimators for bounds defined by conditional linear programs. The first directly solves the conditional linear programs with plugin estimates and uses output from standard LP solvers to de-bias the plugin estimate, avoiding the need for computationally demanding vertex enumeration of all possible solutions for symbolic bounds. The second uses entropic regularization to create smooth approximations to the conditional linear programs, trading a small amount of approximation error for improved estimation and computational efficiency. We establish conditions for asymptotic normality of both estimators, show that both estimators are robust to first-order errors in estimating the conditional constraints and objectives, and construct Wald-type confidence intervals for the partially identified parameters. These results also extend to policy learning problems where the value of a decision policy is only partially identified. We apply our methods to a study on the effects of Medicaid enrollment.