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

This paper is concerned with cross-sectional dependence arising because observations are
interconnected through an observed network. Following (Doukhan and Louhichi, 1999), we …

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 …

When estimating a global average treatment effect (GATE) under network interference, units
can have widely different relationships to the treatment depending on a combination of the …

This article introduces a measure of the diffusion of binary outcomes over a large, sparse
network, when the diffusion is observed in two time periods. The measure captures the …

This survey study discusses main aspects to optimal estimation methodologies for panel
data regression models. In particular, we present current methodological developments for …

We consider a causal inference model in which individuals interact in a social network and
they may not comply with the assigned treatments. In particular, we suppose that the form of …

This paper studies inference in models of discrete choice with social interactions when the
data consists of a single large network. We provide theoretical justification for the use of …

This paper investigates the case of interference, when a unit's treatment also affects other
units' outcome. When interference is at work, policy evaluation mostly relies on the use of …

In this paper, we introduce a method of generating bootstrap samples with unknown patterns
of cross‐sectional/spatial dependence, which we call the spatial dependent wild bootstrap …