Title: Conducting Simulations in Causal Inference with Networks-Based Structural Equation Models,
Abstract: The past decade has seen an increasing body of literature devoted to the
estimation of causal effects in network-dependent data. However, the validity
of many classical statistical methods in such data is often questioned. There
is an emerging need for objective and practical ways to assess which causal
methodologies might be applicable and valid in network-dependent data. This
paper describes a set of tools implemented in the simcausal R package that
allow simulating data based on user-specified structural equation model for
connected units. Specification and simulation of counterfactual data is
implemented for static, dynamic and stochastic interventions. A new interface
aims to simplify the specification of network-based functional relationships
between connected units. A set of examples illustrates how these simulations
may be applied to evaluation of different statistical methods for estimation of
causal effects in network-dependent data.