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CAREER: Robust Causal And Statistical Inference In High Dimensional Structured Systems With Hidden Variables

职业：具有隐藏变量的高维结构化系统中的稳健因果和统计推断

基本信息

批准号：
1942239
负责人：
Ilya Shpitser
金额：
$ 55万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942239&HistoricalAwards=false
关键词：
CAREER Robust Causal Statistical Inference

项目摘要

Understanding causes and effects is crucial in empirical science, however observed associations do not always have clear causal explanations. For instance, hospital patients who were prescribed antibiotics also tend to suffer from opportunistic infections, though we don’t expect antibiotics to cause infections. The likely explanation in this case is that an early presence of an infectious agent caused the latter infection, and the doctor prescribing antibiotics. A key obstacle to finding valid cause-effect relationships from data is the presence of hidden but causally relevant variables, like the infectious agent above. This project aims to develop new methods for drawing valid causal inferences in datasets with hidden variables while avoiding known pitfalls of existing approaches. Aside from its role in data analysis, causal literacy is an important skill for making informed choices as citizens and consumers. The investigator aims to promote this skill by incorporating causal methods into existing data science courses at Johns Hopkins University, developing a new course that will teach methods for detecting misunderstandings of causal claims, and developing a tutorial aimed at bridging the gap between machine learning and statistics in discussing and working on causal inference.Directed acyclic graphs (DAGs) are an elegant method for reasoning about fully observed causal systems. The proposed research aims to provide a new formalism for causal systems with hidden variables that retains the advantages of DAGs, while dispensing with the disadvantages of representing hidden variables directly. This formalism captures all equality constraints in the observed marginal distribution via a regular model of a mixed graph. Success in the proposed research will significantly advance understanding of all major tasks in causal systems with hidden variables: identification, estimation, and computationally efficient probabilistic calculations. As a test bed for methodological developments, the investigator will use a dataset of electronic health records obtained in partnership with the Malone Center for Engineering in Healthcare and the Johns Hopkins Department of Surgery.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

理解因果关系在经验科学中至关重要，然而观察到的关联并不总是有明确的因果解释。例如，医院里开了抗生素的病人也容易受到机会性感染的影响，尽管我们不认为抗生素会引起感染。在这种情况下，可能的解释是，早期存在的传染性病原体导致了后一次感染，医生开了抗生素。从数据中找到有效的因果关系的一个关键障碍是存在隐藏但因果相关的变量，如上面的感染因子。该项目旨在开发新的方法，在隐藏变量的数据集中进行有效的因果推理，同时避免现有方法的已知缺陷。除了在数据分析中的作用外，因果素养是公民和消费者做出知情选择的重要技能。调查人员旨在通过将因果方法纳入约翰霍普金斯大学现有的数据科学课程来促进这一技能，开发一门新课程，将教授检测因果索赔误解的方法，并开发了一个教程，旨在弥合机器学习和统计之间的差距，讨论和研究因果推理。有向无环图（DAG）是一个优雅的方法推理充分观察因果系统。拟议的研究旨在提供一个新的形式主义的因果系统的隐变量，保留了DAG的优点，同时免除直接表示隐变量的缺点。这种形式主义通过混合图的正则模型捕获了所观察到的边际分布中的所有等式约束。在拟议的研究中的成功将显着推进因果系统中的所有主要任务的理解与隐藏变量：识别，估计和计算效率的概率计算。作为方法学发展的测试平台，研究者将使用与马龙医疗保健工程中心和约翰霍普金斯外科部合作获得的电子健康记录数据集。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Causal Discovery in Linear Latent Variable Models Subject to Measurement Error

DOI：
10.48550/arxiv.2211.03984
发表时间：
2022-11
期刊：
ArXiv
影响因子：
0
作者：
Yuqin Yang;AmirEmad Ghassami;Mohamed S. Nafea;N. Kiyavash;Kun Zhang;I. Shpitser
通讯作者：
Yuqin Yang;AmirEmad Ghassami;Mohamed S. Nafea;N. Kiyavash;Kun Zhang;I. Shpitser

Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference

一类双鲁棒泛函的极小极大核机器学习及其在近端因果推理中的应用

DOI：
发表时间：
2022
期刊：
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics
影响因子：
0
作者：
Amiremad Ghassami, Andrew Ying
通讯作者：
Amiremad Ghassami, Andrew Ying

The Lauritzen-Chen Likelihood For Graphical Models

DOI：
发表时间：
2022-07
期刊：
影响因子：
0
作者：
I. Shpitser
通讯作者：
I. Shpitser

Partial Identifiability in Discrete Data with Measurement Error

具有测量误差的离散数据的部分可辨识性

DOI：
发表时间：
2021
期刊：
Proceedings of the Thirty Seventh Conference on Uncertainty in Artificial Intelligence
影响因子：
0
作者：
Noam Finkelstein;Roy Adams;Suchi Saria;Ilya Shpitser
通讯作者：
Ilya Shpitser

Path dependent structural equation models

路径相关结构方程模型

DOI：
发表时间：
2021
期刊：
Proceedings of the Thirty Seventh Conference on Uncertainty in Artificial Intelligence
影响因子：
0
作者：
Ranjani Srinivasan;Jaron J. R. Lee;Rohit Bhattacharya;Ilya Shpitser
通讯作者：
Ilya Shpitser