Causal Explanation Under Indeterminism: A Sampling Approach.

Area of Science:

• Computational causal inference
• Explainable AI
• Health informatics

Background:

• Explaining specific events is crucial for responsibility and decision-making.
• Current computational causal inference methods for event explanation are largely theoretical and lack practical application.
• Vast datasets offer potential for understanding behavior and improving health through causal inference.

Purpose of the Study:

• To develop a novel algorithm for calculating the strength of token causes for event explanation.
• To evaluate the algorithm's performance against prior methods using simulated data.
• To demonstrate the algorithm's applicability in a realistic health scenario.

Main Methods:

• Development of an algorithm to quantify the strength of token causes.
• Evaluation using simulated data, including classic causal structures (chains, common cause, backup causation).
• Application to a simulation of type 1 diabetes to explain hyperglycemic episodes.

Main Results:

• The algorithm successfully identified correct causal relationships in established test cases.
• The approach demonstrated accuracy in explaining complex events in a realistic type 1 diabetes simulation.
• Objective comparison against prior methods and ground truth was enabled by the simulation-based evaluation.

Conclusions:

• The proposed algorithm provides a practical method for explaining specific events using computational causal inference.
• This work advances the field by offering a quantifiable approach to causal explanation.
• The findings have implications for improving health and understanding behavior through data-driven causal insights.