Identification of causal intervention effects under contagion
Xiaoxuan Cai, Wen Wei Loh, Forrest W. Crawford

TL;DR
This paper develops new methods to identify causal effects of interventions in infectious disease transmission, accounting for realistic contagion dynamics, and demonstrates their application in vaccine trial simulations.
Contribution
It introduces causal intervention effects under arbitrary transmission dynamics and provides nonparametric identification methods for empirical trials.
Findings
New causal estimands for infectious disease interventions
Identification of effects using time-to-infection and binary data
Simulation demonstrates applicability in HIV vaccine trial
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…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Bayesian Inference · COVID-19 epidemiological studies
