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].
Reasoning about Causal Models with Infinitely Many Variables
Authors: Joseph Y. Halpern, Spencer Peters5668-5675
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a sound and complete axiomatization of causal reasoning in GSEMs that is an extension of the sound and complete axiomatization provided by Halpern (2000) for SEMs. Considering GSEMs helps clarify what properties Halpern s axioms capture. |
| Researcher Affiliation | Academia | Joseph Y. Halpern, Spencer Peters Cornell University EMAIL, EMAIL |
| Pseudocode | No | The paper defines axioms and logical rules but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code. The URL provided is for the paper itself on arXiv, not for any code. |
| Open Datasets | No | This is a theoretical paper focused on axiomatization; it does not involve datasets or training. |
| Dataset Splits | No | This is a theoretical paper focused on axiomatization; it does not involve datasets or validation splits. |
| Hardware Specification | No | This is a theoretical paper; it does not describe any experimental setup or hardware used. |
| Software Dependencies | No | This is a theoretical paper; it does not describe any experimental setup or software dependencies. |
| Experiment Setup | No | This is a theoretical paper; it does not describe any experimental setup. |