2024/02/20 by Lu Deng, Y. G. Li, Deng, Lu +7
Mathematics · #Applications (stat.AP) #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2402.12653
openalex publication_date 2024/02/20 · openalex created_date 2024/02/22 · openalex updated_date 2026/07/28
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.