Approximate Causal Abstraction.

Area of Science:

• Causal inference
• Philosophy of science
• Artificial intelligence

Background:

• Scientific models exist at various abstraction levels.
• Exact abstraction for causal models was previously developed by Beckers and Halpern (2019).
• Real-world systems often involve approximate, not exact, abstractions.

Purpose of the Study:

• To extend the exact abstraction account to approximate causal models.
• To address discrepancies between different levels of causal models.
• To provide an account of how one causal model approximates another.

Main Methods:

• Extending the exact abstraction framework to handle approximations.
• Analyzing the discrepancies between low-level and high-level causal models.
• Incorporating probabilistic elements into approximate causal abstractions.

Main Results:

• Developed a framework for approximate causal abstractions.
• Showcased how to manage differences between abstract and fundamental causal models.
• Provided a method for understanding causal model approximation.
• Extended the framework to probabilistic causal models, identifying sources of uncertainty.

Conclusions:

• Approximate abstractions are crucial for realistic scientific modeling.
• The developed framework offers a robust way to handle abstraction in causal inference.
• This work advances the understanding of model approximation and uncertainty in causal reasoning.