Linear Causal Discovery with Interventional Constraints.

Area of Science:

• Causal inference and machine learning
• Biomedical data analysis

Background:

• Causal models are crucial for treatment design and understanding biological mechanisms.
• Existing causal discovery methods struggle to incorporate high-level causal knowledge, leading to potential inaccuracies.

Purpose of the Study:

• To introduce and formalize 'interventional constraints' for causal discovery.
• To improve the accuracy and interpretability of causal models by incorporating known causal effects.

Main Methods:

• Developed a novel concept of interventional constraints, distinct from interventional data.
• Formulated the problem as a constrained optimization task for linear causal models.
• Employed a two-stage constrained optimization method to solve the task.

Main Results:

• Interventional constraints ensure learned causal models align with known causal influences.
• The approach demonstrated improved model accuracy and consistency with established findings on real-world datasets.
• Facilitated the discovery of novel causal relationships, reducing the cost of identification.

Conclusions:

• Interventional constraints offer a powerful way to refine causal models by integrating existing causal knowledge.
• This method enhances model explainability and aids in discovering new, previously hidden, causal links.