Dynamical Modeling as a Tool for Inferring Causation.

ABSTRACT: Dynamical models, commonly used in infectious disease epidemiology, are formal mathematical representations of time-changing systems or processes. For many chronic disease epidemiologists, the link between dynamical models and predominant causal inference paradigms is unclear. In this commentary, we explain the use of dynamical models for representing causal systems and the relevance of dynamical models for causal inference. In certain simple settings, dynamical modeling and conventional statistical methods (e.g., regression-based methods) are equivalent, but dynamical modeling has advantages over conventional statistical methods for many causal inference problems. Dynamical models can be used to transparently encode complex biological knowledge, interference and spillover, effect modifica

SUBMITTER: Ackley SF 

PROVIDER: S-EPMC8897986 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature