“Causal Inference for Multiple Non-Randomized Treatments using Fractional Factorial Designs,” Nicole Pashley, Harvard University
QUANTITATIVE RESEARCH METHODS WORKSHOP
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 connect our design-based methods to standard regression methods. We illustrate the benefits of using this method as well as the challenges of this type of data through application.
Nicole Pashley is a PhD candidate in statistics at Harvard University. She is funded as a National Science Foundation Graduate Research Fellow. She is interested in both better understanding causal inference within experimental settings and bringing experimental tools to observational studies. Her work includes exploration of classical designs in casual inference, development of methods for complicated observational data with multiple treatments, and examination of the use of conditionality in causal inference.
This workshop series is being sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.
Open to the Yale community only.