III: Small: pCAR: Discovering and Leveraging Plausibly Causal (p-causal) Relationships to Understand Complex Dynamic Systems

今天，要成功应对社会经济关键领域（如可持续性、公共卫生和生物学）的许多紧迫挑战，需要从高维数据中更深入地了解各种实体之间的因果关系和相互作用。例如，一种流行病的出现和传播涉及到实体之间因果关系复杂的相互作用，包括个人及其社会互动、物理的短程和长程流动网络以及决策者的干预决策（如关闭学校或限制流动）。为了填补这一重要漏洞，并使应用和服务具有重大的经济和公共卫生影响，该项目开展了因果意识数据驱动的科学和工程研究，包括数据支持的因果分析。该项目还有助于劳动力的增长，为博士，硕士和本科水平的研究和教学提供了良好的环境。未来的研究人员都在数据分析和管理的技能正在接受培训，学生们正在通过参与研究，出版物和与领域专家的合作关系被引入职业道路。在这个项目中，研究小组假设数据分析提供了机会，以确定可能导致的关系。他们进一步假设，数据可以用来加强和削弱因果关系的假设，并修剪关系，肯定不是因果关系。 为了验证和利用这些假设，他们引入了“合理因果关系”的新概念。（p-因果关系）和“可能因果关系”（p-因果）关系，他们开发技术来（i）发现p-因果相互作用和关系，（ii）在因果关系本身随时间演变的系统中维持这些p-因果关系，和（iii）使用发现的p-因果关系来支持高效和有效的数据分析，以研究复杂的动态系统。特别是，他们开发了新的模型来捕捉上下文敏感的因果关系，（p-因果）关系，并设计新颖的和可扩展的因果感知数据分析算法，该算法利用不同的和潜在的演进上下文内的实体之间的已知或假设的p-因果关系来处理数据稀疏，不精确，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。

Today, successfully tackling many urgent challenges insocio-economically critical domains (such as sustainability, publichealth, and biology) requires obtaining a deeper understanding of causalrelationships and interactions among a diverse spectrum of entities fromhigh-dimensional data. For example, the emergence and propagation of anepidemic involves a causally complex interplay of entities, includingindividuals and their social-interactions, physical short-range andlong-range networks of mobility, and intervention decisions (such asschool closures or restrictions on mobility) by decision makers. Withthe aim of filling this important hole and enabling applications andservices with significant economic and public health impact, the project carriesout research on causally-aware data-driven science and engineering,including data-supported causal analysis. The project also contributesto workforce growth, provides an excellent context for doctoral,master's, and undergraduate level research and teaching. Futureresearchers that have the skills in both data analysis and managementare being trained and students are being introduced to career pathwaysthrough their participation in research, publications, and partnershipswith domain experts.In this project, the research team hypothesizes that data analysis providesopportunities for identifying relationships that can potentially becausal. They further hypothesize that data can be used for strengtheningand weakening causal assumptions, and for pruning relationships that arecertainly not causal. To validate and leverage these hypotheses, theyintroduce the novel concepts of 'plausible causality' (p-causality) and'plausibly causal (p-causal) relationships' and they developtechniques to (i) discover p-causal interactions and relationships, (ii)maintain these p-casual relationships in systems where causality itselfevolves over time, and (iii) use discovered p-causal relationships tosupport efficient and effective data analytics to study complex, dynamicsystems. In particular, they develop new models to capturecontext-sensitive plausibly causal (p-causal) relationships in complex,dynamic systems and design novel and scalable causally-awaredata analysis algorithms that leverage known or hypothesized p-causalrelationships among entities within different and potentially evolvingcontexts to deal with data sparsity, imprecision, and noise.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.