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The past decade has seen an increasing body of literature devoted to theestimation of causal effects in network-dependent data. However, the validityof many classical statistical methods in such data is often questioned. Thereis an emerging need for objective and practical ways to assess which causalmethodologies might be applicable and valid in network-dependent data. Thispaper describes a set of tools implemented in the simcausal R package thatallow simulating data based on user-specified structural equation model forconnected units. Specification and simulation of counterfactual data isimplemented for static, dynamic and stochastic interventions. A new interfaceaims to simplify the specification of network-based functional relationshipsbetween connected units. A set of examples illustrates how these simulationsmay be applied to evaluation of different statistical methods for estimation ofcausal effects in network-dependent data.

Conducting Simulations in Causal Inference with Networks-Based Structural Equation Models