Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks

Abstract

Biological networks are a very convenient modelling and visualisation tool to discover knowledge from modern high-throughput genomics and postgenomics data sets. Indeed, biological entities are not isolated, but are components of complex multi-level systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.

A review of causal discovery methods for molecular network analysis

“…Since then, a large number of causal inference methods have been developed using omics data. This approach is advantageous in the study of biology as it allows for inferring causality without interventions, especially when randomised controlled trials are infeasible due to high cost and ethical issues (White & Vignes, 2019).…”