The article discusses the limitations of existing causal identification techniques based on classical probability theory and introduces a new approach using symmetric monoidal categories. By focusing on the syntax of causal models, the authors propose a purely algorithmic description for causal identification. The process involves manipulating signatures to derive interventional distributions by fixing operations. The application of this method is demonstrated through examples of back-door and front-door adjustments in complex causal models.
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by Dhurim Cakiq... às arxiv.org 03-15-2024
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