B0984
Title: A design-based Riesz representation framework for randomized experiments
Authors: Fredrik Savje - Yale University (United States) [presenting]
Christopher Harshaw - UC Berkeley (United States)
Yitan Wang - Yale University (United States)
Abstract: A new design-based framework is described for drawing causal inference in randomized experiments. Estimands in the framework are defined as arbitrary linear functionals of the potential outcome functions. This makes the framework expressive, allowing experimenters to formulate and investigate a wide range of causal questions. We describe a class of estimators for estimands defined using the framework and investigate their properties. The construction of the estimators is based on insights from the Riesz representation theorem. We provide necessary and sufficient conditions for unbiasedness and consistency. Finally, we provide conditions under which the estimators are asymptotically normal, and describe a conservative variance estimator to facilitate inference about the estimands.
