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Probabilities of causation and post-infection outcomes

Published: April 25, 2025 | arXiv ID: 2504.17992v1

By: Bronner P. Gonçalves

Potential Business Impact:

Helps doctors understand why people get sick after infections.

Business Areas:
A/B Testing Data and Analytics

Probabilities of causation provide explanatory information on the observed occurrence (causal necessity) and non-occurrence (causal sufficiency) of events. Here, we adapt these probabilities (probability of necessity, probability of sufficiency, and probability of necessity and sufficiency) to an important class of epidemiologic outcomes, post-infection outcomes. A defining feature of studies on these outcomes is that they account for the post-treatment variable, infection acquisition, which means that, for individuals who remain uninfected, the outcome is not defined. Following previous work by Hudgens and Halloran, we describe analyses of post-infection outcomes using the principal stratification framework, and then derive expressions for the probabilities of causation in terms of principal strata-related parameters. Finally, we show that these expressions provide insights into the contributions of different processes (absence or occurrence of infection, and disease severity), implicitly encoded in the definition of the outcome, to causation.

Country of Origin
🇬🇧 United Kingdom

Page Count
33 pages

Category
Quantitative Biology:
Quantitative Methods