DAG-informed regression modelling, agent-based modelling and microsimulation modelling: a critical comparison of methods for causal inference
| Autor: | Arnold, KF, Harrison, WJ, Heppenstall, AJ, Gilthorpe, MS |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Counterfactual thinking Systems Analysis Counterfactual conditional Epidemiology Microsimulation Context (language use) microsimulation modelling 03 medical and health sciences 0302 clinical medicine Methods Econometrics Humans 030212 general & internal medicine causal inference Causal model Models Statistical directed acyclic graphs Confounding Factors Epidemiologic Regression analysis General Medicine agent-based modelling counterfactuals Causality Mathematical theory 030104 developmental biology Research Design Causal inference |
| Zdroj: | International Journal of Epidemiology |
| ISSN: | 1464-3685 0300-5771 |
| DOI: | 10.1093/ije/dyy260 |
| Popis: | The current paradigm for causal inference in epidemiology relies primarily on the evaluation of counterfactual contrasts via statistical regression models informed by graphical causal models (often in the form of directed acyclic graphs, or DAGs) and their underlying mathematical theory. However, there have been growing calls for supplementary methods, and one such method that has been proposed is agent-based modelling due to its potential for simulating counterfactuals. However, within the epidemiological literature, there currently exists a general lack of clarity regarding what exactly agent-based modelling is (and is not) and, importantly, how it differs from microsimulation modelling—perhaps its closest methodological comparator. We clarify this distinction by briefly reviewing the history of each method, which provides a context for their similarities and differences, and casts light on the types of research questions that they have evolved (and thus are well suited) to answering; we do the same for DAG-informed regression methods. The distinct historical evolutions of DAG-informed regression modelling, microsimulation modelling and agent-based modelling have given rise to distinct features of the methods themselves, and provide a foundation for critical comparison. Not only are the three methods well suited to addressing different types of causal questions, but, in doing so, they place differing levels of emphasis on fixed and random effects, and also tend to operate on different timescales and in different timeframes. |
| Databáze: | OpenAIRE |
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