CAREER: Learning from Observational Data with Knowledge

来自社交网络、气候学、金融和其他领域的大型观测数据集使研究人员有可能测试复杂的假设，而这些假设是以前的研究无法解决的。在生物学和健康领域尤其如此，随着收集长期人口数据的新方法的普及，例如从电子医疗记录中收集数据，以及从身体佩戴的传感器收集真实世界的健康数据。然而，可以在具有数百或数千个变量的数据集中测试的复杂假设的数量远远超过人类可以提出和推理的数量。穷尽地测试所有可能的关系在计算上是不可行的，并且在此测试之后，研究人员仍然必须检查非平凡数量的看似重要的发现，以确定哪些仍然需要实验验证。该项目的具体目标是推断因果关系，因为这些关系不仅可以深入了解系统的行为方式，还可以了解它的行为原因，从而能够制定成功的干预措施。 这项工作的成果将通过大学课程和高中生暑期课程纳入三级教育（高中、本科和研究生）。除了交流因果推理的核心概念外，暑期课程还将向潜在的计算机科学家介绍计算机科学研究的关键领域。将开发的方法应用于中风和糖尿病的数据可能会导致对中风恢复的生理过程的新认识，以及影响糖尿病患者血糖的因素之间的复杂相互作用，这项工作将通过实验和先验知识之间的反馈回路，从大规模数据集中更强大和有效地推断因果关系。目前的方法要求用户指定要测试的变量和假设的集合，但是这些将结果限制为用户选择探索的集合。相反，这项工作将开发可以使用因果关系形式的先验知识以及先前的实验结果来约束将要测试的内容并生成新假设的方法。原因提供了关于其影响的信息，这些信息不包含在其他变量中，因此这项工作将开发原因解释性的度量以及它产生的信息量，并使用该度量的变化来指导约束假设空间中复杂关系的生成。 所提出的方法与随机概率论的不同之处在于，新方法将是确定性的，并将单独评估关系，从而解决计算挑战并减少不正确推断的影响。 其次，这项工作将导致算法可以自动评估发现与先验知识的关系，无论它们是一致的，新颖的还是矛盾的。这将使研究人员能够更深入地关注可能重要或有趣的发现，而不是那些简单地确认先前知识的发现。 它还提供了知识和推理之间的反馈回路。

Large observational datasets from social networks, climatology, finance, and other areas have made it possible for researchers to test complex hypotheses that previous studies would have been under-powered to tackle. This is especially true in biology and health, with the proliferation of new methods for gathering long-term population data, such as from electronic medical records, and real-world health data from body-worn sensors. However, the number of complex hypotheses that can be tested in datasets with hundreds or thousands of variables far surpasses what humans can propose and reason about. Exhaustively testing all possible relationships is not computationally feasible, and after this testing a researcher must still examine a non-trivial number of seemingly significant findings to determine which still need to be validated experimentally. This project aims specifically to infer causal relationships, as these provide insight into not only how a system behaves, but also why it behaves as it does, enabling the development of successful interventions. Results from this work will be incorporated into education at three levels (high school, undergraduate, and graduate) through university courses and summer programs for high school students. In addition to communicating the core concepts of causal inference, the summer programs will also introduce potential computer scientists to key areas of computer science research. Applications of the methods developed to data from stroke and diabetes may lead to new knowledge about the physiologic processes underlying recovery in stroke, and the complex interaction of factors affecting glucose in people with diabetes.This work will lead to more robust and efficient inference of causal relationships from large-scale datasets, through a feedback loop between experiments and prior knowledge. Current approaches require users to specify the set of variables and hypotheses to be tested, but these limit findings to the set a user chose to explore. Instead this work will develop methods that can use prior knowledge in the form of causal relationships as well as prior experimental results to constrain what will be tested and generate new hypotheses. Causes provide information about their effect that are not contained in other variables, so this work will develop measures of how explanatory a cause is and how much information it yields, and use changes in this measure to guide generation of complex relationships in the constrained hypothesis space. The proposed approach differs from stochastic heuristics in that the new method will be deterministic, and will evaluate relationships individually, thus addressing the computational challenge and reducing the impact of incorrect inference. Second, the work will lead to algorithms that can automatically evaluate how findings relate to prior knowledge, whether they are, for example, consistent, novel, or contradictory. This will allow researchers to focus more in depth on findings likely to be significant or interesting, rather than those that simply confirm prior knowledge. It also provides a feedback loop between knowledge and inference.