Causal effects in microbiomes using interventional calculus.

ABSTRACT: Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques

SUBMITTER: Sazal M 

PROVIDER: S-EPMC7970971 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature