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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Conditional Independencies under the Algorithmic Independence of Conditionals
Authors: Jan Lemeire
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we analyze the relationship between faithfulness and the more recent condition of algorithmic Independence of Conditionals (IC) with respect to the Conditional Independencies (CIs) they allow. Both conditions have been extensively used for causal inference by refuting factorizations for which the condition does not hold. ... We prove that on top of the CIs permitted by the Markov condition (faithfulness), IPC allows non-minimality, deterministic relations and what we called proportional CPDs. |
| Researcher Affiliation | Academia | Jan Lemeire EMAIL Vrije Universiteit Brussel, INDI Dept, ETRO Dept. Pleinlaan 2, B-1050 Brussels, Brussels, Belgium |
| Pseudocode | No | The paper primarily focuses on theoretical analysis, definitions, theorems, lemmas, and proofs related to conditional independencies and algorithmic independence. It does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the methodology described, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and uses conceptual examples (e.g., 'X Y Z', 'Fig. 3') to illustrate concepts. It does not describe any experiments that utilize specific datasets, nor does it provide access information for any open datasets. |
| Dataset Splits | No | As the paper is theoretical and does not perform experiments with datasets, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical and conceptual analysis. It does not mention the use of any specific software components or libraries with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and conceptual analysis. It does not describe any experimental setups, hyperparameters, or training configurations. |