🇬🇧 Causal Inference under interference: methodological challenges and some case studies

Abstract

We introduce methods for studying for causal effects in
settings characterized by interference, arising when the
outcome of a unit (e.g., patient’s infection) depends not
only on their own treatment (e.g., vaccination) but also on
the treatment of others (e.g., friends).

We first introduce estimands and explore identification
and estimation strategies in experimental and
observational settings. We then focus on important but
nonstandard settings having two distinct sets of units:
units to which the intervention is applied and units on
which the outcomes are measured, which is called
bipartite interference: treatments applied to one
intervention unit can affect multiple outcome units, and
the outcome of a unit may depend on the treatments
applied to multiple intervention units. We illustrate the
methods with some case studies