Pearl’s Causality in a Logical Setting

Authors: Alexander Bochman, Vladimir Lifschitz

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Reproducibility Variable Result LLM Response
Research Type Theoretical We provide a logical representation of Pearl s structural causal models in the framework of the causal calculus of Mc Cain and Turner (1997) and its first-order generalization by Lifschitz. It will be shown that, under this representation, the nonmonotonic semantics of the causal calculus describes precisely the solutions of the structural equations (the causal worlds of a causal model), while the causal logic from Bochman (2004) is adequate for describing the behavior of causal models under interventions (forming submodels).
Researcher Affiliation Academia Alexander Bochman Computer Science Department Holon Institute of Technology, Israel bochmana@hit.ac.il Vladimir Lifschitz Department of Computer Science University of Texas at Austin vl@cs.utexas.edu
Pseudocode No The paper does not contain structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any concrete access to source code for the methodology described.
Open Datasets No The paper is theoretical and does not involve the use of datasets for training.
Dataset Splits No The paper is theoretical and does not involve dataset splits for validation.
Hardware Specification No The paper is theoretical and does not provide any hardware specifications for running experiments.
Software Dependencies No The paper is theoretical and does not provide specific ancillary software details with version numbers.
Experiment Setup No The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations.