  Simulation methods are among the most ubiquitous methodological tools in
statistical science. In particular, statisticians often is simulation to
explore properties of statistical functionals in models for which developed
statistical theory is insufficient or to assess finite sample properties of
theoretical results. We show that the design of simulation experiments can be
viewed from the perspective of causal intervention on a data generating
mechanism. We then demonstrate the use of causal tools and frameworks in this
context. Our perspective is agnostic to the particular domain of the simulation
experiment which increases the potential impact of our proposed approach. In
this paper, we consider two illustrative examples. First, we re-examine a
predictive machine learning example from a popular textbook designed to assess
the relationship between mean function complexity and the mean-squared error.
Second, we discuss a traditional causal inference method problem, simulating
the effect of unmeasured confounding on estimation, specifically to illustrate
bias amplification. In both cases, applying causal principles and using
graphical models with parameters and distributions as nodes in the spirit of
influence diagrams can 1) make precise which estimand the simulation targets ,
2) suggest modifications to better attain the simulation goals, and 3) provide
scaffolding to discuss performance criteria for a particular simulation design.
