Emulating Target Trials to Improve Causal Inference From Agent-Based Models

被引:5
|
作者
Murray, Eleanor J. [1 ]
Marshall, Brandon D. L. [2 ]
Buchanan, Ashley L. [3 ]
机构
[1] Boston Univ, Dept Epidemiol, Sch Publ Hlth, 715 Albany St, Boston, MA 02118 USA
[2] Brown Univ, Dept Epidemiol, Sch Publ Hlth, Providence, RI 02912 USA
[3] Univ Rhode Isl, Coll Pharm, Dept Pharm Practice, Kingston, RI 02881 USA
关键词
agent-based models; causal inference; interference; spillover; target; GENERALIZING EVIDENCE;
D O I
10.1093/aje/kwab040
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Agent-based models are a key tool for investigating the emergent properties of population health settings, such as infectious disease transmission, where the exposure often violates the key "no interference" assumption of traditional causal inference under the potential outcomes framework. Agent-based models and other simulation-based modeling approaches have generally been viewed as a separate knowledge-generating paradigm from the potential outcomes framework, but this can lead to confusion about how to interpret the results of these models in real-world settings. By explicitly incorporating the target trial framework into the development of an agent-based or other simulation model, we can clarify the causal parameters of interest, as well as make explicit the assumptions required for valid causal effect estimation within or between populations. In this paper, we describe the use of the target trial framework for designing agent-based models when the goal is estimation of causal effects in the presence of interference, or spillover.
引用
收藏
页码:1652 / 1658
页数:7
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