  No man is an island, as individuals interact and influence one another daily
in our society. When social influence takes place in experiments on a
population of interconnected individuals, the treatment on a unit may affect
the outcomes of other units, a phenomenon known as interference. This thesis
develops a causal framework and inference methodology for experiments where
interference takes place on a network of influence (i.e. network interference).
In this framework, the network potential outcomes serve as the key quantity and
flexible building blocks for causal estimands that represent a variety of
primary, peer, and total treatment effects. These causal estimands are
estimated via principled Bayesian imputation of missing outcomes. The theory on
the unconfoundedness assumptions leading to simplified imputation highlights
the importance of including relevant network covariates in the potential
outcome model. Additionally, experimental designs that result in balanced
covariates and sizes across treatment exposure groups further improve the
causal estimate, especially by mitigating potential outcome model
mis-specification. The true potential outcome model is not typically known in
real-world experiments, so the best practice is to account for interference and
confounding network covariates through both balanced designs and model-based
imputation. A full factorial simulated experiment is formulated to demonstrate
this principle by comparing performance across different randomization schemes
during the design phase and estimators during the analysis phase, under varying
network topology and true potential outcome models. Overall, this thesis
asserts that interference is not just a nuisance for analysis but rather an
opportunity for quantifying and leveraging peer effects in real-world
experiments.
