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Activity Number: 250 - Bayesian Modeling, Infectious Diseases and Tracking
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #307384 Presentation
Title: Identification of Causal Effects Under Contagion
Author(s): Xiaoxuan Cai* and Forrest W Crawford and Wen Wei Loh
Companies: Yale University and Yale School of Public Health and Ghent University
Keywords: Causal inference; Infectious disease; Survival analysis

Causal inference under interference is becoming a vital part of applied statistics, especially in infectious disease trails and social intervening program. Interference may take the form of “spillover” effect when outcome depends on other individuals’ treatments; Alternatively, when the outcome itself is transmissible, the outcome depends on other individual’s treatments and outcomes. Researchers have proposed a wide variety of causal estimands and statistical approaches to study the causal relationship under interference. However, most estimands are defined under particular designs and are not compatible with each other. In this work, we propose a more generic way of defining causal estimands for infectious disease outcome in the example of partnership models. Combining tools from traditional causal inference, competing risk and structural transmission models, we further develop parametric, non-parametric and semi-parametric statistical models and inferential procedures for estimation, which involve multiple time scales and permit regression adjustment for covariates. Large-sample statistical properties are established, and performance of the models are verified by simulations.

Authors who are presenting talks have a * after their name.

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