@misc{10481/80485,
year = {2023},
month = {1},
url = {https://hdl.handle.net/10481/80485},
abstract = {The COVID-19 pandemic has spurred a large amount of experimental and observational studies
reporting clear correlation between the risk of developing severe COVID-19 (or dying from
it) and whether the individual is male or female. This paper is an attempt to explain the
supposed male vulnerability to COVID-19 using a causal approach. We proceed by identifying
a set of confounding and mediating factors, based on the review of epidemiological literature
and analysis of sex-dis-aggregated data. Those factors are then taken into consideration to
produce explainable and fair prediction and decision models from observational data. The
paper outlines how non-causal models can motivate discriminatory policies such as biased
allocation of the limited resources in intensive care units (ICUs). The objective is to anticipate
and avoid disparate impact and discrimination, by considering causal knowledge and causalbased
techniques to compliment the collection and analysis of observational big-data. The hope
is to contribute to more careful use of health related information access systems for developing
fair and robust predictive models.},
organization = {ERC grant Hypatia under the European Union's Horizon 2020 research and innovation programme	835294},
organization = {Spanish Government Juan de la Cierva Incorporacion contract	IJC2019-039152-I},
organization = {Google Research Scholar Grant},
organization = {Marie Sklodowska-Curie Actions (MSCA) Postdoctoral Fellowship	101059332},
organization = {Universidad de Granada/CBUA},
publisher = {Elsevier},
keywords = {Explainability},
keywords = {Causality},
keywords = {Causal fairness},
keywords = {COVID-19},
keywords = {Sex},
keywords = {Gender},
keywords = {Equality},
keywords = {Artificial intelligence},
keywords = {Healthcare},
title = {Gender and sex bias in COVID-19 epidemiological data through the lens of causality},
doi = {10.1016/j.ipm.2023.103276},
author = {Díaz Rodríguez, Natalia Ana and Binkyte, Ruta and Bakkali, Wafae and Bookseller, Sannidhi and Tubaro, Paola and Bacevičius, Andrius and Zhioua, Sami and Chatila, Raja},
}