Causal inference using hierarchical and nonparametric Bayesian interaction models
Abstract
Recent research bridging statistics and machine learning suggests potential for great practical advantages by combining tools associated separately with prediction, generalization, and causal inference. This project includes evaluation of algorithms currently at the intersection of machine learning and causal inference, development of better models, development and implementationof computational algorithms for fitting these models, and testing these methods in real and simulated data.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2017
- Source ID
- N000141712141
Entities
People
- Andrew Gelman
Organizations
- Office of Naval Research
- Trustees of Columbia University in the City of New York
- United States Navy