Mitigating pathogenesis for target discovery and disease subtyping.

Area of Science:

• Causal inference
• Biomedical data analysis
• Computational biology

Background:

• Current treatment effect definitions overlook pathogenic mechanisms.
• Understanding how treatments impact disease root causes is crucial for effective intervention.
• There is a need for metrics that quantify treatment effects on underlying disease etiology.

Purpose of the Study:

• Introduce the Treated Root causal Effects (TRE) metric to measure treatment modification of root causal effects.
• Develop an algorithm for automated treatment target discovery and patient subtyping using TREs.
• Provide an interpretable framework for causal effect estimation in treatment analysis.

Main Methods:

• Developed a partially linear causal model to extract root causal effects.
• Estimated Treated Root causal Effects (TREs) for target discovery and patient subtyping.
• Ensured interpretability without assuming an invertible structural equation model.

Main Results:

• The proposed TRE metric effectively quantifies treatment impact on root causal effects.
• Automated identification of treatment targets and patient clusters based on TREs was demonstrated.
• The approach showed generality across diverse datasets.

Conclusions:

• Treated Root causal Effects (TRE) offers a novel way to evaluate treatments by focusing on pathogenic mechanisms.
• TREs facilitate personalized medicine through automated target discovery and patient stratification.
• The method provides an interpretable and broadly applicable framework for causal treatment effect analysis.