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].
A Rule-Based Modal View of Causal Reasoning
Authors: Emiliano Lorini
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a novel rule-based semantics for causal reasoning as well as a number of modal languages interpreted over it. They enable us to represent some fundamental concepts in the theory of causality including causal necessity and possibility, interventionist conditionals and Lewisian conditionals. We provide complexity results for the satisfiability checking and model checking problem for these modal languages. |
| Researcher Affiliation | Academia | Emiliano Lorini IRIT, CNRS, Toulouse University, France EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. It uses illustrative examples (e.g., Example 1: 'social influence in a multi-agent setting') but not public datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation or dataset splits. |
| Hardware Specification | No | The paper does not describe any specific hardware used for its work. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |