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Identification of causal intervention effects under contagion

Authors :
Cai Xiaoxuan
Loh Wen Wei
Crawford Forrest W.
Source :
Journal of Causal Inference, Vol 9, Iss 1, Pp 9-38 (2021)
Publication Year :
2021
Publisher :
De Gruyter, 2021.

Abstract

Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment – such as a vaccine – given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural assumptions that preclude realistic infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial.

Details

Language :
English
ISSN :
21933677 and 21933685
Volume :
9
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Journal of Causal Inference
Publication Type :
Academic Journal
Accession number :
edsdoj.9794b9e305e64ae0bdd6c1fa3c751dbd
Document Type :
article
Full Text :
https://doi.org/10.1515/jci-2019-0033