- Aday, Sean
- Atkins, Patricia
- Augustine, Nancy
- Bell, Michael
- Brunori, David
- Clarke, Lindsay
- Cordes, Joseph
- Friedman, Samantha
- Friedman, Julia
- Green, Richard
- Joyce, Phil
- Keeley, Melissa
- Kubrin, Charis
- Maltzman, Forrest
- Murphy, Teresa
- Schneider, Jo Anne
- Sigelman, Lee
- Snyder, Chris
- Squires, Gregory
- Stone, Clarence
- Wilnat, Lar
- Wiseman, Michael
- Wolman, Hal
- Young, Garry
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Research - Langche Zeng
This page features research funded through GWIPP and performed by Langche Zeng
Title: Improving Prediction and Causal Inference with Graphical Methods and Models
Researcher(s): Langche Zeng (Dept. of Political Science, GWU)
Funding Source: The National Science Foundation
Research Status: Current
Summary:
This project explores the utility of various graphical models and methods, in particular causal graph theory, social networks and random graphs for relational data, in improving prediction and causal inference in empirical political and social sciences. Graphical models are naturally suited for conceptualizing and representing relationships, and graphical methods provide promising tools for studying structural properties of political and social systems. The project also seeks to extend the methods to accommodate special features of social science data, such as functional complexity and rareness of certain events.
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