Estimation of Causal Peer Influence Effects
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):1489-1497, 2013.
The broad adoption of social media has generated interest in leveraging peer influence for inducing desired user behavior. Quantifying the causal effect of peer influence presents technical challenges, however, including how to deal with social interference, complex response functions and network uncertainty. In this paper, we extend potential outcomes to allow for interference, we introduce well-defined causal estimands of peer-influence, and we develop two estimation procedures: a frequentist procedure relying on a sequential randomization design that requires knowledge of the network but operates under complicated response functions, and a Bayesian procedure which accounts for network uncertainty but relies on a linear response assumption to increase estimation precision. Our results show the advantages and disadvantages of the proposed methods in a number of situations.