Wednesday, November 6, 2019
CGIS Knafel Building (K354) - 12-1:30 pm
Abstract: We explore a framework for addressing causal questions in an observational setting with multiple treatments. This setting involves attempting to approximate an experiment from observational data. With multiple treatments, this experiment would be a factorial design. However, certain treatment combinations may be so rare that, for some combinations, we have no measured outcomes in the observed data. We propose to conceptualize a hypothetical fractional factorial experiment instead of a full factorial experiment and lay out a framework for analysis in this setting. We also connect our design-based methods to standard regression methods. We illustrate the method and the challenges of this type of data through application.