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

University Of Washington, Seattle WA

Investigators

Abstract

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.