Alam Receives NSF CAREER Award for Causal Inference in Large-Scale Studies
Assistant Professor Md. Noor E Alam, of the Department of Mechanical and Industrial Engineering, received a $500K National Science Foundation CAREER Award for developing “Robust Matching Algorithms for Causal Inference in Large Observational Studies.” The research will utilize the power of Big Data to infer causality in large-scale observational studies. It 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.
Assistant Prof. Alam is also director of the Decision Analytics Lab at Northeastern University, and holds a faculty associate position at Northeastern’s Centre for Health Policy and Healthcare Research and an aﬃliated faculty position both at Northeastern’s Global Resilience Institute, and School of Public Policy and Urban Affairs. His research focus is in the areas of applied operations research, healthcare, supply chain, large scale optimization, and big data analytics. Prior joining to Northeastern in 2015, Dr. Alam was a Postdoctoral Research Fellow in the Sloan School of Management at Massachusetts Institute of Technology. He completed his PhD in Engineering Management in the Department of Mechanical Engineering at the University of Alberta. Most recently, he served as a board of director for Logistics and Supply Chain division of Institute of Industrial and Systems Engineers (IISE) in the year 2018 to 2020.
Abstract Source: NSF
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.