Automated Causal Discovery with Observational Data via Directed Graphical Models - New Theory and Methods

建立因果关系在许多科学领域都至关重要，包括生物学、心理学、神经科学、气候科学、机器人技术和量子力学。虽然建立因果关系的黄金标准仍然是受控实验，但它可能是昂贵的，不道德的，甚至在许多情况下是不可能的。因此，从被动观察的数据（而不是实验数据）建立因果关系往往是可取的，有时也是唯一的选择。在这个项目中，PI将开发一系列因果发现方法，这些方法在理论上是合理的，在实际中对通过观测数据确定因果关系是有用的。将开发与拟议方法配套的高效开放源码软件，该项目还为研究生提供研究培训机会。所提出的方法将基于有向图模型（DGMs）。尽管DGMs在各学科中很受欢迎，但由于一些突出的挑战，使用DGMs从观测数据中建立因果关系在理论上和方法上仍然很困难。首先，由于马尔可夫等价类，DGMs通常是不可识别的，其中所有DGMs编码相同的条件独立性集合，因此在没有进一步假设的情况下无法相互区分。第二，DGM类在边缘化下并不封闭，因此结构学习可能会被不可测量的混杂因素所误导。第三，绝大多数现有的方法依赖于相对较强的分布假设的数据生成机制，这可能会导致重大的估计偏差时，严重违反假设。该项目旨在通过开发新的非iid数据的DGMs，并在存在混杂因素和模型错误指定的情况下建立其因果可识别性理论来解决这三个挑战。作为验证，所提出的方法将用于从基因组数据集反向工程基因调控网络。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Establishing causality is crucial in many fields of science including biology, psychology, neuroscience, climate science, robotics, and quantum mechanics. While the gold standard for establishing causality remains controlled experimentation, it can be expensive, unethical, and even impossible in many cases. Therefore, establishing causality from passively observed data (as opposed to experimental data) is often desirable and, sometimes, the only option. In this project, the PI will develop a series of causal discovery methods that are theoretically sound and practically useful for identifying causality with observational data. Efficient open-source software accompanying the proposed methods will be developed and the project also provides research training opportunities for graduate students. The proposed methods will be based on directed graphical models (DGMs). Despite the popularity of DGMs across disciplines, using DGMs to establish causality from observational data remains difficult, both theoretically and methodologically, due to several prominent challenges. First, DGMs are generally non-identifiable due to Markov equivalence class in which all DGMs encode the same set of conditional independencies and hence are not distinguishable from each other without further assumptions. Second, the class of DGMs is not closed under marginalization and therefore the structure learning can be misled by unmeasured confounders. Third, the vast majority of existing methods rely on relatively strong distributional assumptions on the data generating mechanism, which can cause significant estimation biases when the assumptions are seriously violated. This project aims to address these three challenges by developing new DGMs for non-iid data and establishing their causal identifiability theories in the presence of confounders and model misspecification. As validation, the proposed methods will be used to reverse engineer gene regulatory networks from genomic datasets. Results will be disseminated through workshops, publications, and new graduate courses.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.