Causal inference, social networks and chain graphs.

ABSTRACT: Traditionally, statistical inference and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and outcomes may be contagious. Existing models proposed for causal inference using observational data from networks of interacting individuals have two major shortcomings. First, they often require a level of granularity in the data that is infeasible in practice to collect in most settings and, second, the models are high dimensional and often too big to fit to the available data. We illustrate a

SUBMITTER: Ogburn EL 

PROVIDER: S-EPMC8313030 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature