Leveraging Background Knowledge for Identification and Estimation of Causal Effects in the Presence of Latent Variables

这个项目的重点是使用观测数据和专家知识进行因果推理。估计因果效应是许多科学努力的目标，通常，唯一可用的数据是来自观察性研究的数据。然而，仅仅基于观测数据来估计因果效应并不总是可能的。一种利用专家知识和观测数据的混合方法可能有助于估计因果效应或缩小可能估计的范围。这种混合方法目前仅限于假设因果系统中的所有变量都是观察和测量的。对于许多现实世界的应用程序来说，这一假设往往过于严格。该项目将开发使用专家知识的方法，以帮助在存在隐藏变量的情况下从观测数据中估计因果影响。该项目开发的方法将立即适用于广泛的科学学科，最著名的是流行病学、经济学、个性化医学和算法公平性研究。该项目的研究基于概率图形模型，该模型可用于表示观察到的变量集上的条件独立关系。一般而言，仅基于观测数据，因果图形模型最多只能识别到此类图形的等价类别。该项目的第一个目标是开发一套完整的规则，用于根据某些因果关系的专家知识更新一套兼容的图形模型。该项目的第二个目标是根据最新的一套模型制定确定和估计因果关系的图形标准。此外，研究人员将从计算和统计效率的角度研究更新后的等价类别。这一奖项反映了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。

This project focuses on causal inference using observational data and expert knowledge. Estimating causal effects is a goal of many scientific endeavors, and often, the only available data are those from observational studies. However, estimating causal effects based on observational data alone is not always possible. A hybrid method that leverages both expert knowledge and observational data may help estimate a causal effect or narrow down a range of likely estimates. Such hybrid methods are currently limited to assuming that all variables in the causal system are observed and measured. This assumption is often too stringent for many real-world applications. This project will develop methods for using expert knowledge to help estimate causal effects from observational data in the presence of hidden variables. The methods developed in this project would be immediately applicable to an extensive range of scientific disciplines, most notably epidemiology, economics, personalized medicine, and the study of algorithmic fairness.The research in this project is grounded on probabilistic graphical models that can be used to represent conditional independence relationships on the observed set of variables. In general, based on observational data alone, the causal graphical model can be identified only up to an equivalence class of such graphs. The first goal of the project is to develop a complete set of rules for updating the set of compatible graphical models based on expert knowledge of certain causal relationships. The second goal of the project concerns the development of graphical criteria for causal effect identification and estimation based on the updated set of models. Furthermore, the investigator will study the updated equivalence class in terms of computational and statistical efficiencies.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.