Analysis of cause-effect inference by comparing regression errors.

Area of Science:

• Causal Inference
• Machine Learning
• Statistics

Background:

• Inferring causal relationships from observational data is a fundamental challenge in many scientific disciplines.
• Existing methods often rely on complex assumptions or extensive data.

Purpose of the Study:

• To develop a straightforward and computationally efficient algorithm for causal direction inference.
• To validate the algorithm's performance against established causal inference techniques.

Main Methods:

• Comparing least-squares prediction errors in both possible causal directions (X -> Y and Y -> X).
• Leveraging assumptions of independence between the causal function, conditional noise, and cause distribution.
• Developing an algorithm based on error minimization in the correct causal direction.

Main Results:

• The proposed method demonstrates smaller prediction errors in the true causal direction under specific conditions (equal scaling, near-deterministic relations).
• The algorithm's performance is comparable to existing methods on various synthetic and real-world datasets.

Conclusions:

• The developed algorithm offers a practical approach to causal discovery.
• Its effectiveness is demonstrated under clear assumptions, providing a valuable tool for researchers.