  In social media platforms, user behavior is often influenced by interactions
with other users, complicating the accurate estimation of causal effects in
traditional A/B experiments. This study investigates situations where an
individual's outcome can be broken down into the sum of multiple pairwise
outcomes, a reflection of user interactions. These outcomes, referred to as
dyadic data, are prevalent in many social network contexts. Utilizing a
Bernoulli randomized design, we introduce a novel unbiased estimator for the
total treatment effect (TTE), which quantifies the difference in population
mean when all individuals are assigned to treatment versus control groups. We
further explore the bias of our estimator in scenarios where it is impractical
to include all individuals in the experiment, a common constraint in online
control experiments. Our numerical results reveal that our proposed estimator
consistently outperforms some commonly used estimators, underscoring its
potential for more precise causal effect estimation in social media
environments.
