Invited Commentary: Agent-Based Models-Bias in the Face of Discovery.Agent-based models (ABMs) have grown in popularity in epidemiologic applications, but the assumptions necessary for valid inference have only partially been articulated.  In particular, their work focused on the biases that can arise in ABMs that use parameters drawn from distinct populations whose causal structures and baseline outcome risks differ.  This demonstration of the quantitative issues that arise in transporting effects between populations has implications not only for ABMs but for all epidemiologic applications, because making use of epidemiologic results requires application beyond a study sample. 
ABMs
2017;186(2):131-142) provided a much-needed analysis of the consequence of some of these assumptions, comparing analysis using an ABM to a similar analysis using the parametric g-formula. 
the parametric g-formula
Invited Commentary: Agent-Based Models-Bias in the Face of Discovery.Agent-based models (ABMs) have grown in popularity in epidemiologic applications, but the assumptions necessary for valid inference have only partially been articulated.  In particular, their work focused on the biases that can arise in ABMs that use parameters drawn from distinct populations whose causal structures and baseline outcome risks differ.  This demonstration of the quantitative issues that arise in transporting effects between populations has implications not only for ABMs but for all epidemiologic applications, because making use of epidemiologic results requires application beyond a study sample. 
ABMs
Invited Commentary: Agent-Based Models-Bias in the Face of Discovery.Agent-based models (ABMs) have grown in popularity in epidemiologic applications, but the assumptions necessary for valid inference have only partially been articulated.  In particular, their work focused on the biases that can arise in ABMs that use parameters drawn from distinct populations whose causal structures and baseline outcome risks differ.  This demonstration of the quantitative issues that arise in transporting effects between populations has implications not only for ABMs but for all epidemiologic applications, because making use of epidemiologic results requires application beyond a study sample. 
ABMs