The Kohl Centre at Virginia Tech
Our mission is to harness the full potential of modern data analytics tools to address a diverse spectrum of challenges spanning agribusiness, food and health, environmental concerns, public policy dilemmas, and the vast landscape of applied economics. At the Kohl Centre, we integrate the rigor and power of economic thinking with cutting-edge advancements in causal inference and machine learning to yield robust, data-driven evidence needed for social progress and innovation.
Contact
Le Wang, Ph.D.
Professor and David M. Kohl Chair
lewangecon@vt.edu
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- Faculty affiliates
- The people behind The Kohl Centre
The DSPG platform promotes team science, where undergraduates and graduate students collaborate with faculty members to address current social issues locally and nationally. In the summer, the teams are brought together to conduct research at the intersection of statistics, computation, and the social sciences to determine how information can be leveraged to improve quality of life and inform public policy.
Our team-based experiential learning approach develops the problem-solving, leadership, and technical skills necessary for a new generation of workers in food, agriculture, and community development. The project-focused program provides all participating undergraduates with an opportunity to experience how data science tools are applied to meaningful research problems confronting agriculture and rural communities. >>> Learn more
The department develops the specialized skills and knowledge needed in economic modeling, data analytics, econometrics, forecasting, and micro- and macroeconomics. Our approach integrates tools and frameworks with industry and government issues, complemented by applied coursework in commodity markets, pricing, and contemporary issues and responses in food systems. >>> Learn more
- "Causal Inference: Framework and Toolkit for Evidence-based Policymaking." Hosted by STEP (Science, Technology, and Engineering in Policy) > Details
- Virginia Tech Department of Statistics: "Instrumental Variable Estimation in Causal Machine Learning: Foundation, Application, and Theoretical Advance"
- Symposium on the Human-Centered Future of Work > Details