Causal Seminar: Fredrik Sävje, Yale University

A Design-Based Riesz Representation Framework for Randomized Experiments

We describe a new design-based framework for drawing causal inference in randomized experiments. Causal effects in the framework are defined as linear functionals evaluated at potential outcome functions. Knowledge and assumptions about the potential outcome functions are encoded as function spaces. 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 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 the construction of confidence intervals for the estimands.

Reading Group: 3:00 PM – 3:45 PM EST

Pre-reading: Design and analysis of bipartite experiments under a linear exposure-response model

Speaker:

• Fredrik Sävje, Assistant Professor of Political Science and of Statistics and Data Science, Yale University

Fredrik Sävje is an assistant professor of political science and of statistics and data science. His primary research interest is whether causal inferences can be drawn from data.