Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters
因果参数双重去偏机器学习的任意时点有效推断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：摘要以 DML 学习因果参数，并讨论局部平均处理效应及平均处理效应的部分识别，直接对应因果推断。
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…
因果参数双重去偏机器学习的任意时点有效推断
- 研究问题
- 在研究者可根据数据继续采样或提前停止时，如何把固定样本量下的 DML 推断扩展为对数据依赖停止时点仍有效的推断？
- 方法与思路
- 作者建立 DML 渐近结果的时间一致版本，在略强于通常 DML 的条件下构造任意停止时点的区间，并讨论两类因果应用。
- 材料陈述的贡献
- 摘要称现有 DML 方法经少量修改即可获得任意时点有效保证，并以不依从实验的局部平均效应及观察研究的部分识别演示。
- 阅读关注点
- 应核对时间一致保证的渐近含义、辅助估计与序贯采样条件、停止规则适用范围，以及部分识别时区间覆盖的目标。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未列明加强条件，不能把时间一致渐近结果直接视为有限样本精确保证。
原文摘要
Double (debiased) machine learning (DML) has seen widespread use in recent years for learning causal/structural parameters, in part due to its flexibility and adaptability to high-dimensional nuisance functions as well as its ability to avoid bias from regularization or overfitting. However, the classic double-debiased framework is only valid asymptotically for a predetermined sample size, thus lacking the flexibility of collecting more data if sharper inference is needed, or stopping data collection early if useful inferences can be made earlier than expected. This can be of particular concern in large scale experimental studies with huge financial costs or human lives at stake, as well as in observational studies where the length of confidence of intervals do not shrink to zero even with increasing sample size due to partial identifiability of a structural parameter. In this paper, we present time-uniform counterparts to the asymptotic DML results, enabling valid inference and confidence intervals for structural parameters to be constructed at any arbitrary (possibly data-dependent) stopping time. We provide conditions which are only slightly stronger than the standard DML conditions, but offer the stronger guarantee for anytime-valid inference. This facilitates the transformation of any existing DML method to provide anytime-valid guarantees with minimal modifications, making it highly adaptable and easy to use. We illustrate our procedure using two instances: a) local average treatment effect in online experiments with non-compliance, and b) partial identification of average treatment effect in observational studies with potential unmeasured confounding.