Mathematical model of causal inference in social networks

Abstract

In this article, we model the effects of machine learning algorithms on different Social Network users by using a causal inference framework, making estimation about the underlying system and design systems to control underlying latent unobservable system. In this case, the latent internal state of the system can be a wide range of interest of user. For example, it can be a user's preferences for some certain products or affiliation of the user to some political parties. We represent these variables using state space model. In this model, the internal state of the system, e.g. the preferences or affiliations of the user is observed using user's connections with the Social Networks such as Facebook status updates, shares, comments, blogs, tweets etc.