Active Learning for Optimal Intervention Design in Causal Models
因果模型中最优干预设计的主动学习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：摘要围绕干预后分布、因果模型更新和顺序实验选择展开，直接对应因果推断中的干预设计。
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…
因果模型中最优干预设计的主动学习
- 研究问题
- 当可选干预空间很大时，如何通过顺序实验寻找使干预后均值接近目标均值的干预，同时减少所需实验样本？
- 方法与思路
- 作者对因果模型进行贝叶斯更新，以可闭式计算的因果采集函数选择干预，并在线性且因果图已知的模型中分析理论性质。
- 材料陈述的贡献
- 摘要称给出信息论界与一致性结果，并在合成和单细胞数据中展示采集函数通常以更少样本找到所需干预的表现。
- 阅读关注点
- 应核对最优目标、可允许的干预和理论条件，尤其是已知因果图限制，并检查实验比较的样本预算及目标误差指标。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出理论条件与数值详情，不能将一致性推广到未知图或任意非线性模型。
原文摘要
Sequential experimental design to discover interventions that achieve a desired outcome is a key problem in various domains including science, engineering and public policy. When the space of possible interventions is large, making an exhaustive search infeasible, experimental design strategies are needed. In this context, encoding the causal relationships between the variables, and thus the effect of interventions on the system, is critical for identifying desirable interventions more efficiently. Here, we develop a causal active learning strategy to identify interventions that are optimal, as measured by the discrepancy between the post-interventional mean of the distribution and a desired target mean. The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for fast optimization. The resulting algorithms are theoretically grounded with information-theoretic bounds and provable consistency results for linear causal models with known causal graph. We apply our approach to both synthetic data and single-cell transcriptomic data from Perturb-CITE-seq experiments to identify optimal perturbations that induce a specific cell state transition. The causally informed acquisition function generally outperforms existing criteria allowing for optimal intervention design with fewer but carefully selected samples.