Latent Agents in Networks: Estimation and Targeting

Abstract

Latent Agents in Networks: Estimation and Targeting In “Latent Agents in Networks: Estimation and Targeting,” Baris Ata, Alexandre Belloni, and Ozan Candogan address the problem of estimating network effects in a setting in which data only on a subset of agents is available. In this setting, the observable agents influence each other’s outcomes both directly and indirectly through their influence on the latent agents. Even in sparse networks, the combination of direct and indirect network effects yields a nonsparse influence structure that makes estimation challenging. The authors overcome this challenge and provide an estimation algorithm that performs well in high-dimensional settings. They also establish convergence rates for their proposed estimator and show that their performance guarantees are valid for a large class of networks. Finally, the authors demonstrate the application of their algorithm to a targeted advertising problem, in which it can be used to obtain asymptotically optimal advertising decisions despite the presence of latent agents.