Towards Effective Causal Partitioning by Edge Cutting of Adjoint Graph

Area of Science:

• Causal inference
• Machine learning
• Graph theory

Background:

• Causal partitioning is crucial for causal discovery using a divide-and-conquer strategy.
• Existing heuristic methods often fail to achieve accurate partitioning without violating d-separation, hindering causal inference.
• This limitation necessitates the development of improved causal partitioning techniques.

Purpose of the Study:

• To develop a novel and effective method for causal partitioning in causal discovery.
• To address the limitations of existing methods in achieving accurate partitioning while respecting d-separation.
• To enhance the performance of causal inference through improved partitioning.

Main Methods:

• Transforming causal partitioning into an alternative, more solvable problem.
• Constructing a superstructure graph (G) from observed data (D) using conditional independence (CI) tests.
• Leveraging point-line duality to obtain an adjoint graph (G_A) and minimizing edge-cut ratio on G_A.

Main Results:

• The proposed method yields a valid causal partitioning with a smaller causal-cut ratio on G.
• The method successfully avoids violating d-separation, a key requirement for valid causal graphs.
• Extensive experiments demonstrate superior performance compared to existing causal partitioning methods.

Conclusions:

• The novel approach offers a significant advancement in causal partitioning for causal discovery.
• By avoiding d-separation violations, the method ensures more reliable causal inference.
• The efficient algorithm and demonstrated effectiveness make it a valuable tool for researchers.