Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Pearlโs Causality in a Logical Setting
Authors: Alexander Bochman, Vladimir Lifschitz
AAAI 2015 | Venue PDF | LLM Run Details
| 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 ๏ฌrst-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 EMAIL Vladimir Lifschitz Department of Computer Science University of Texas at Austin EMAIL |
| 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. |