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

理解因果关系在经验科学中是至关重要的，然而观察到的关联并不总是有明确的因果解释。例如，开了抗生素的医院患者也往往患有机会性感染，尽管我们不认为抗生素会导致感染。在这种情况下，可能的解释是，早期存在的感染源导致了后一种感染，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具有因果关系的变量，如上面的感染源。该项目旨在开发新的方法来在具有隐藏变量的数据集中提取有效的因果推理，同时避免现有方法的已知陷阱。除了在数据分析中的作用，因果识字也是作为公民和消费者做出明智选择的一项重要技能。这位研究人员的目标是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，教授检测因果声明的误解的方法，并开发一门教程，旨在弥合机器学习和统计学之间的差距，讨论和研究因果推理。有向无环图(DAG)是一种关于完全观察到的因果系统的优雅推理方法。该研究旨在为具有隐藏变量的因果系统提供一种新的形式化方法，既保留了DAG的优点，又克服了直接表示隐藏变量的缺点。这种形式通过混合图的规则模型捕获了观察到的边际分布中的所有等式约束。拟议研究的成功将极大地促进对具有隐藏变量的因果系统中所有主要任务的理解：识别、估计和计算高效的概率计算。作为方法开发的试验台，研究人员将使用与马龙医疗工程中心和约翰霍普金斯大学外科部门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，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.