CausalTrail: Testing hypothesis using causal Bayesian networks.

Area of Science:

• Computational Biology
• Bioinformatics
• Systems Biology

Background:

• Causal Bayesian Networks (CBNs) model causal relationships and intervention effects.
• Existing tools primarily focus on constructing CBNs, with limited options for downstream analysis.

Purpose of the Study:

• To introduce CausalTrail, a novel software tool for performing downstream analysis on causal Bayesian networks.
• To enable causal inference and intervention effect computation using the do-calculus.

Main Methods:

• CausalTrail implements causal reasoning using the do-calculus.
• The tool supports multiple data import methods and handles missing data.
• It features a flexible query language and a graphical user interface.

Main Results:

• CausalTrail provides a dedicated platform for downstream analysis of CBNs.
• The software effectively computes intervention effects, crucial for understanding system dynamics.
• Its ability to handle missing data makes it suitable for complex multi-omics studies.

Conclusions:

• CausalTrail addresses a gap in available software for causal Bayesian network analysis.
• The tool facilitates robust causal inference and intervention analysis, particularly in multi-omics research.
• CausalTrail is available as open-source software for broader scientific application.