Identification of causal intervention effects under contagion
- Author
- Xiaoxuan Cai, Wen Wei Loh (UGent) and Forrest W. Crawford
- Organization
- 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.
- Keywords
- Statistics, Probability and Uncertainty, Statistics and Probability, infectiousness, interference, mediation, susceptibility, transmission, vaccine, INFECTIOUS-DISEASE DATA, TIME-VARYING EXPOSURES, BAYESIAN-MCMC APPROACH, MEDIATION ANALYSIS, VACCINE EFFICACY, TRANSMISSION, HOUSEHOLD, INFERENCE, INFLUENZA, DESIGN
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8725482
- MLA
- Cai, Xiaoxuan, et al. “Identification of Causal Intervention Effects under Contagion.” JOURNAL OF CAUSAL INFERENCE, vol. 9, no. 1, 2021, pp. 9–38, doi:10.1515/jci-2019-0033.
- APA
- Cai, X., Loh, W. W., & Crawford, F. W. (2021). Identification of causal intervention effects under contagion. JOURNAL OF CAUSAL INFERENCE, 9(1), 9–38. https://doi.org/10.1515/jci-2019-0033
- Chicago author-date
- Cai, Xiaoxuan, Wen Wei Loh, and Forrest W. Crawford. 2021. “Identification of Causal Intervention Effects under Contagion.” JOURNAL OF CAUSAL INFERENCE 9 (1): 9–38. https://doi.org/10.1515/jci-2019-0033.
- Chicago author-date (all authors)
- Cai, Xiaoxuan, Wen Wei Loh, and Forrest W. Crawford. 2021. “Identification of Causal Intervention Effects under Contagion.” JOURNAL OF CAUSAL INFERENCE 9 (1): 9–38. doi:10.1515/jci-2019-0033.
- Vancouver
- 1.Cai X, Loh WW, Crawford FW. Identification of causal intervention effects under contagion. JOURNAL OF CAUSAL INFERENCE. 2021;9(1):9–38.
- IEEE
- [1]X. Cai, W. W. Loh, and F. W. Crawford, “Identification of causal intervention effects under contagion,” JOURNAL OF CAUSAL INFERENCE, vol. 9, no. 1, pp. 9–38, 2021.
@article{8725482,
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.}},
author = {{Cai, Xiaoxuan and Loh, Wen Wei and Crawford, Forrest W.}},
issn = {{2193-3685}},
journal = {{JOURNAL OF CAUSAL INFERENCE}},
keywords = {{Statistics,Probability and Uncertainty,Statistics and Probability,infectiousness,interference,mediation,susceptibility,transmission,vaccine,INFECTIOUS-DISEASE DATA,TIME-VARYING EXPOSURES,BAYESIAN-MCMC APPROACH,MEDIATION ANALYSIS,VACCINE EFFICACY,TRANSMISSION,HOUSEHOLD,INFERENCE,INFLUENZA,DESIGN}},
language = {{eng}},
number = {{1}},
pages = {{9--38}},
title = {{Identification of causal intervention effects under contagion}},
url = {{http://doi.org/10.1515/jci-2019-0033}},
volume = {{9}},
year = {{2021}},
}
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