The Methodology and Measurement initiative concentrates on the development, testing, and dissemination of statistical techniques to understand real-world issues. Selected projects include:
- Building and extending methods to explore the sensitivity of inferences of causal effects to changes in structural assumptions, and creating practical guidelines for using sensitivity analyses in applied settings.
- Developing nonparametric Bayesian models at the intersection of machine learning and causal inference, and formulating computational techniques to fit these models.
- Applying modern psychometric methods to improve the reliability and validity of in-class observational measures of teaching, and connecting these improvements to advances in the application of value-added modeling.
