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DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of

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  • 1Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.

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|December 7, 2018
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Summary

Agent-based modeling offers a novel approach to causal inference in epidemiology, complementing traditional regression models. Understanding its distinctions from microsimulation is key for applying these simulation methods effectively.

Keywords:
agent-based modellingcausal inferencecounterfactualsdirected acyclic graphsmicrosimulation modelling

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Area of Science:

  • Epidemiology
  • Computational Modeling
  • Causal Inference

Background:

  • Traditional causal inference in epidemiology relies on statistical regression models informed by graphical causal models (DAGs).
  • There is a growing need for supplementary methods to address complex causal questions.
  • Agent-based modeling (ABM) has been proposed as a potential tool for simulating counterfactuals.

Purpose of the Study:

  • To clarify the nature and distinctions of agent-based modeling within epidemiology.
  • To differentiate agent-based modeling from microsimulation modeling, its closest methodological comparator.
  • To compare agent-based modeling, microsimulation modeling, and DAG-informed regression methods.

Main Methods:

  • Historical review of agent-based modeling, microsimulation modeling, and DAG-informed regression.
  • Comparative analysis of the evolution, features, and applications of these three methods.
  • Examination of how each method addresses different types of causal questions and their emphasis on effects, timescales, and timeframes.

Main Results:

  • Agent-based modeling, microsimulation modeling, and DAG-informed regression have distinct historical evolutions and features.
  • Each method is suited for different types of causal inference research questions.
  • These methods differ in their emphasis on fixed vs. random effects, and their operational timescales.

Conclusions:

  • Agent-based modeling offers a valuable, distinct approach to causal inference in epidemiology.
  • Understanding the differences between ABM, microsimulation, and regression is crucial for appropriate method selection.
  • These diverse modeling approaches provide a richer toolkit for epidemiological causal inference.