Automated Causal Discovery with Observational Data via Directed Graphical Models - New Theory and Methods

Texas A&M University, College Station TX

Investigators

Abstract

Establishing causality is crucial in many fields of science including biology, psychology, neuroscience, climate science, robotics, and quantum mechanics. While the gold standard for establishing causality remains controlled experimentation, it can be expensive, unethical, and even impossible in many cases. Therefore, establishing causality from passively observed data (as opposed to experimental data) is often desirable and, sometimes, the only option. In this project, the PI will develop a series of causal discovery methods that are theoretically sound and practically useful for identifying causality with observational data. Efficient open-source software accompanying the proposed methods will be developed and the project also provides research training opportunities for graduate students. The proposed methods will be based on directed graphical models (DGMs). Despite the popularity of DGMs across disciplines, using DGMs to establish causality from observational data remains difficult, both theoretically and methodologically, due to several prominent challenges. First, DGMs are generally non-identifiable due to Markov equivalence class in which all DGMs encode the same set of conditional independencies and hence are not distinguishable from each other without further assumptions. Second, the class of DGMs is not closed under marginalization and therefore the structure learning can be misled by unmeasured confounders. Third, the vast majority of existing methods rely on relatively strong distributional assumptions on the data generating mechanism, which can cause significant estimation biases when the assumptions are seriously violated. This project aims to address these three challenges by developing new DGMs for non-iid data and establishing their causal identifiability theories in the presence of confounders and model misspecification. As validation, the proposed methods will be used to reverse engineer gene regulatory networks from genomic datasets. Results will be disseminated through workshops, publications, and new graduate courses. 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.