Explaining hidden mechanisms: a generative model for causal graphs with nonlinear latent factors.

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Biomedical data analysis generates complex causal graphs that are difficult for humans to interpret mechanistically.
• Existing methods struggle to provide interpretable insights from condition-specific causal networks.

Purpose of the Study:

• To develop a generative model for inferring interpretable latent causal mechanisms from observed causal graphs.
• To propose an inference method, Truncated Reconstruction based Interpretable Prediction (TRIP), that enhances mechanistic interpretability and predictive performance.

Main Methods:

• Formulated a structural-equation-based generative model with a bidirectional causal-consistency constraint.
• Developed the TRIP inference method using orthonormal projection and joint optimization of prediction and graph-reconstruction losses.
• Validated TRIP on synthetic benchmarks and EMT-related gene regulatory networks.

Main Results:

• TRIP successfully recovers nonlinear latent mechanisms from complex causal graph families.
• The method improves predictive performance compared to existing approaches.
• TRIP organizes large condition-specific networks into interpretable mechanistic axes for hypothesis generation.

Conclusions:

• TRIP offers a powerful approach to bridge the gap between complex causal graph analysis and mechanistic biological understanding.
• The method enhances interpretability across different abstraction levels, facilitating biological discovery.
• TRIP's ability to organize and simplify complex networks aids in hypothesis generation for further research.