Causal aggregation: estimation and inference of causal effects by constraint-based data fusion
因果聚合：基于约束的数据融合与联合干预效应推断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：摘要核心是融合实验与观察数据识别联合干预效应，并讨论工具变量、无混杂样本及识别条件，因此直接归入因果推断。
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…
因果聚合：基于约束的数据融合与联合干预效应推断
- 研究问题
- 各数据集只干预少数变量、甚至缺乏随机实验时，如何融合不同来源的因果信息，识别多个变量同时干预的联合效应？
- 方法与思路
- 作者以跨数据集因果约束聚合信息，讨论线性效应的识别与推断、高维稀疏估计，并提出类似梯度提升的非线性实验数据聚合方法。
- 材料陈述的贡献
- 摘要称刻画线性与非线性联合效应的可识别条件，提供过度识别下的有效估计及高维方法，并用模拟和半合成数据展示。
- 阅读关注点
- 应核对不同数据集之间可共享哪些机制、已知父节点集合或工具变量的作用、约束不足时的处理，以及非线性聚合的识别要求。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；摘要未列完整融合与识别假设，不能认定任意异质实验或观察数据都能可靠合并。
原文摘要
In causal inference, it is common to estimate the causal effect of a single treatment variable on an outcome. However, practitioners may also be interested in the effect of simultaneous interventions on multiple covariates of a fixed target variable. We propose a novel method that allows to estimate the effect of joint interventions using data from different experiments in which only very few variables are manipulated. If there is only little randomized data or no randomized data at all, one can use observational data sets if certain parental sets are known or instrumental variables are available. If the joint causal effect is linear, the proposed method can be used for estimation and inference of joint causal effects, and we characterize conditions for identifiability. In the overidentified case, we indicate how to leverage all the available causal information across multiple data sets to efficiently estimate the causal effects. If the dimension of the covariate vector is large, we may only have a few samples in each data set. Under a sparsity assumption, we derive an estimator of the causal effects in this high-dimensional scenario. In addition, we show how to deal with the case where a lack of experimental constraints prevents direct estimation of the causal effects. When the joint causal effects are non-linear, we characterize conditions under which identifiability holds, and propose a non-linear causal aggregation methodology for experimental data sets similar to the gradient boosting algorithm where in each iteration we combine weak learners trained on different datasets using only unconfounded samples. We demonstrate the effectiveness of the proposed method on simulated and semi-synthetic data.