Local Interference: Removing Interference Bias in Semi-Parametric Causal Models
局部干扰：半参数因果模型中干扰偏差的消除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：摘要直接讨论个体间处理干扰、去除干扰后的因果效应及其识别边界，因此对应因果推断和网络与干扰两个方向。
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…
局部干扰：半参数因果模型中干扰偏差的消除
- 研究问题
- 当其他个体的处理影响本人的结果时，研究如何定义并识别去除干扰后的平均因果效应，以及这种识别需要何种模型限制。
- 方法与思路
- 作者定义局部干扰因果模型，在允许非线性及协变量相关干扰的部分半参数模型中推导解析识别表达式，并讨论一般模型的不可识别性。
- 材料陈述的贡献
- 摘要称给出真实平均因果效应的识别结果，同时证明任意局部干扰模型不足以保证识别，并使用模拟和真实数据作经验验证。
- 阅读关注点
- 应核对去除干扰所对应的干预含义、局部性的形式定义、半参数限制及所需观测信息，并确认方法是否仍需部分网络知识。
- 阅读边界
- 这是arXiv预印本，同行评审、期刊接收及刊本变化未核验；摘要未说明具体识别限制、估计与推断程序或真实数据细节。
原文摘要
Interference bias is a major impediment to identifying causal effects in real-world settings. For example, vaccination reduces the transmission of a virus in a population such that everyone benefits -- even those who are not treated. This is a source of bias that must be accounted for if one wants to learn the true effect of a vaccine on an individual's immune system. Previous approaches addressing interference bias require strong domain knowledge in the form of a graphical interaction network fully describing interference between units. Moreover, they place additional constraints on the form the interference can take, such as restricting to linear outcome models, and assuming that interference experienced by a unit does not depend on the unit's covariates. Our work addresses these shortcomings. We first provide and justify a novel definition of causal models with local interference. We prove that the True Average Causal Effect, a measure of causality where interference has been removed, can be identified in certain semi-parametric models satisfying this definition. These models allow for non-linearity, and also for interference to depend on a unit's covariates. An analytic estimand for the True Average Causal Effect is given in such settings. We further prove that the True Average Causal Effect cannot be identified in arbitrary models with local interference, showing that identification requires semi-parametric assumptions. Finally, we provide an empirical validation of our method on both simulated and real-world datasets.