CAREER: Robust Matching Algorithms for Causal Inference in Large Observational Studies

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This Faculty Early Career Development Program (CAREER) grant will advance the national health, prosperity, and economic welfare by utilizing the power of big data to infer causality in large-scale observational studies. In many situations, particularly in the public health domain, it may be difficult or prohibitively expensive to design controlled studies to evaluate effective public policies. As large-scale data collection increases, the design of methods to infer causality between treatment and outcome by partitioning observations into appropriate sets has become an attractive alternative. Current methods underlying causal inference suffer from several fundamental challenges that may lead to sub-optimal policy selection. This project will develop tractable computational approaches to facilitate better policy decision making. As an important use case, the project will evaluate policies for improving treatment quality of Opioid Use Disorder (OUD) using large-scale U.S. healthcare data. The integrated education and research plan will attract and involve a diverse student body, from high school through graduate school, in research and practice. Through active engagement with partnering organizations, including community colleges and an HBCU, the project will provide opportunities for members of underrepresented groups in engineering to address pressing societal needs. Using a modern optimization perspective, this project will advance existing methods for causal inference by developing a theoretical and computational framework that encompasses both inference and matching to identify causality from an observational study. The research objectives are to (1) establish a robust causal inference framework with matching methods to reduce uncertainty, (2) ensure covariate balance in high dimensional space, (3) develop optimal covariate balance techniques to reduce bias and model dependency by ensuring desired distributional properties, and (4) evaluate and advance U.S. healthcare policies based on this framework. To this end, a rigorous optimization framework will be employed to explicitly account for uncertainties in causal inference, maintain neighborhood structures of high dimensional data in low dimensions with matching requirements, and ensure optimal distributional properties of observational data. Efficient exact solution algorithms will be developed exploiting problem structure. Scalability will be addressed through algorithmic schemes with desirable convergence properties and data structure-based decomposition methods. These algorithms are expected to be useful to a wide variety of optimization problems such as quadratic assignment, convex-nonlinear feasibility, and binary feasibility.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.