This paper studies causal inference in randomized experiments under network interference.
Commonly used models of interference posit that treatments assigned to alters beyond a …

Researchers across the health and social sciences generally assume that observations are
independent, even while relying on convenience samples that draw subjects from one or a …

The global average treatment effect (GATE) is a primary quantity of interest in the study of
causal inference under network interference. With a correctly specified exposure model of …

This paper studies the design of two-wave experiments in the presence of spillover effects
when the researcher aims to conduct precise inference on treatment effects. We consider …

We consider design-based causal inference in settings where randomized treatments have
effects that bleed out into space in complex ways that overlap and in violation of the …

This article studies the problem of optimally allocating treatments in the presence of spillover
effects, using information from a (quasi-) experiment. I introduce a method that maximizes …

Network experiments have been widely used in investigating interference among units.
Under the``approximate neighborhood interference" framework introduced by\cite …

Identification and estimation of causal peer effects are challenging in observational studies
for two reasons. The first is the identification challenge due to unmeasured network …

Exposure mappings facilitate investigations of complex causal effects when units interact in
experiments. Current methods require experimenters to use the same exposure mappings to …

Since network data commonly consists of observations from a single large network,
researchers often partition the network into clusters in order to apply cluster‐robust inference …