Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks
This work addresses the challenge of causal discovery in biology, but it appears incremental as it applies existing causal formalism to biological contexts without major methodological innovations.
The paper tackles the problem of inferring causal relationships from observational data in biological systems using Bayesian networks, demonstrating the approach on both simulated small networks and a real biological dataset.
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.