What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models

Area of Science:

• Causal inference and machine learning
• Computational and systems biology
• Philosophy of science

Background:

• Discovering causal relationships is crucial for understanding complex systems.
• Identifying the structure of unmeasured sub-mechanisms presents a significant challenge in causal discovery.
• Existing methods often struggle when key variables are not directly observed.

Purpose of the Study:

• To develop a correct algorithm for identifying latent, endogenous structure-sub-mechanisms.
• To address the problem of discovering hidden causal structures when variables are unmeasured.
• To provide a method that can be integrated with existing causal discovery techniques.

Main Methods:

• Utilizing Gebharter's (2014) representation for analyzing unmeasured variables.
• Exploiting Sober's (1998) insight to develop a novel algorithm.
• Focusing on a restricted class of structures for algorithmic correctness.

Main Results:

• A correct algorithm for identifying latent, endogenous structure-sub-mechanisms is provided.
• The algorithm is applicable to a specific, restricted class of structures.
• The method demonstrates potential for integration with other causal discovery approaches.

Conclusions:

• The developed algorithm offers a novel solution for uncovering hidden causal mechanisms.
• This work contributes to the advancement of causal discovery in scenarios with unmeasured variables.
• Future research can explore merging this algorithm with methods for learning causal relations among unmeasured variables and feedback loops.