Collaborative Research: Causal Discovery in the Presence of Measurement Error Theory and Practical Algorithms

协作研究：测量误差理论和实用算法存在下的因果发现

• 批准号: 1829681
• 负责人: Kun Zhang
• 金额: $ 6万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1829681&HistoricalAwards=false
• 关键词: Collaborative; Research; Causal; Discovery; Presence

The discovery of cause-and-effect relationships is a fundamental notion in science. To find such causal relationships, traditional methods based on interventions or randomized experiments are usually expensive or even impossible. Causal discovery aims to find the underlying causal structure or model from purely observational data and has many applications in various disciplines. Despite its successes on a number of real problems, the presence of measurement error in the observed data can produce serious mistakes in the output of various causal discovery methods. Given the ubiquity of measurement error caused by instruments or proxies used in the measuring process, this problem has been recognized as one of the main obstacles to reliable causal discovery. It is still unknown to what extent the causal structure for relevant variables can be identified in the presence of measurement error, let alone how to develop practical algorithms to solve this problem. This project aims to fill the void. It will investigate what information of the causal model of interest can be recovered from observed data and what assumptions one has to make to achieve successful recovery of the causal information. Based on such theoretical results, the project will then investigate efficient estimation procedures. The project will establish theoretical identifiability results for the underlying, true causal structure and, in light of such results, develop practical causal discovery algorithms. Preliminary results show theoretically how measurement error changes the (conditional) independence and dependence relationships in the data, i.e., how the (conditional) independence and independence relations between the observed variables are different from those between the measurement-error-free variables. Based on the preliminary results, several research tasks will be carried out. First, classical causal discovery often assumes a linear-Gaussian model for the data, in which the causal relations are linear and the variables are jointly Gaussian. This project will establish the conditions under which the underlying causal model is identifiable up to an equivalence class or only partially identifiable. Second, this study will investigate how the identifiability of underlying causal structure in the presence of measurement error can actually benefit from the non-Gaussian noise assumption. Third, this study will develop statistically more efficient estimation procedures, by extending the GES method, by exploiting suitable sparsity constraints, or by extending the A* Bayesian network learning procedure. Finally, the above ideas will be extended to deal with related models in causality or statistics, including other contamination models, nonlinear causal models, and Markov networks.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.

因果关系的发现是科学的基本概念。为了找到这种因果关系，基于干预或随机实验的传统方法通常非常昂贵，甚至是不可能的。因果发现旨在从纯粹的观察数据中找到潜在的因果结构或模型，并且在各个学科中有许多应用。尽管它在许多实际问题上取得了成功，但观测数据中测量误差的存在可能会在各种因果发现方法的输出中产生严重错误。鉴于测量过程中使用的仪器或代理引起的测量误差普遍存在，该问题已被认为是可靠因果发现的主要障碍之一。在存在测量误差的情况下，尚不清楚在多大程度上能够识别相关变量的因果结构，更不用说如何开发实用的算法来解决这个问题了。该项目旨在填补这一空白。它将研究可以从观察到的数据中恢复感兴趣的因果模型的哪些信息，以及必须做出哪些假设才能成功恢复因果信息。基于这些理论结果，该项目将研究有效的估算程序。 该项目将为潜在的真实因果结构建立理论可识别性结果，并根据这些结果开发实用的因果发现算法。初步结果从理论上表明了测量误差如何改变数据中的（条件）独立性和依赖性关系，即观测变量之间的（条件）独立性和独立性关系与无测量误差变量之间的（条件）独立性和独立性关系有何不同。根据初步结果，将开展多项研究任务。首先，经典因果发现通常假设数据采用线性高斯模型，其中因果关系是线性的，变量是联合高斯的。该项目将建立一些条件，在这些条件下，底层因果模型可识别到等价类或仅部分可识别。其次，本研究将研究在存在测量误差的情况下潜在因果结构的可识别性如何实际上受益于非高斯噪声假设。第三，本研究将通过扩展 GES 方法、利用合适的稀疏性约束或扩展 A* 贝叶斯网络学习程序，开发统计上更有效的估计程序。最后，上述想法将扩展到处理因果关系或统计中的相关模型，包括其他污染模型、非线性因果模型和马尔可夫网络。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。

项目成果

Causal discovery in the presence of missing data

存在缺失数据时的因果发现

• 发表时间: 2019-04
• 期刊: Proceedings of Machine Learning Research
• 作者: Ruibo Tu; Cheng Zhang
• 通讯作者: Cheng Zhang

On Learning Invariant Representations for Domain Adaptation

关于学习领域适应的不变表示

• DOI: 10.1016/j.jbiomech.2019.109478
• 发表时间: 2019-05-24
• 期刊: Journal of biomechanics
• 影响因子: 2.4
• 作者: H. Zhao;Rémi Tachet des Combes;Kun Zhang;Geoffrey J. Gordon
• 通讯作者: Geoffrey J. Gordon

Causal Discovery with General Non-Linear Relationships Using Non-Linear Independent Component Analysis,

使用非线性独立成分分析发现一般非线性关系的因果关系，

• 发表时间: 2019-01
• 期刊: Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI
• 作者: Ricardo Pio Monti; Kun Zhang
• 通讯作者: Kun Zhang

Triad Constraints for Learning Causal Structure of Latent Variables

学习潜变量因果结构的三元组约束

• DOI: 10.1002/mc.22216
• 发表时间: 2024-09-13
• 期刊: Molecular Carcinogenesis
• 影响因子: 4.6
• 作者: Ruichu Cai;Feng Xie;C. Glymour;Z. Hao;Kun Zhang
• 通讯作者: Kun Zhang

Data-Driven Approach to Multiple-Source Domain Adaptation

数据驱动的多源域适应方法

• 发表时间: 2019-04
• 期刊: Proceedings of Machine Learning Research
• 作者: Petar Stojanov; Mingming Gong
• 通讯作者: Mingming Gong