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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Hierarchy of Graphical Models for Counterfactual Inferences
Authors: Hongshuo Yang, Elias Bareinboim
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper does not include experiments. (Justification for question 4, 5, 6, 7, 8 in NeurIPS Paper Checklist) |
| Researcher Affiliation | Academia | Hongshuo Yang Elias Bareinboim Causal Artificial Intelligence Lab Columbia University EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 AMWN-CONSTRUCT(G, W ) Input: Causal Diagram G and a set of counterfactual variables W Output: GA(W ), the AMWN constructed from G and W... Algorithm 2 CTFIDU(Y , y , Z, G)... Algorithm 3 CTFID(Y , y , X , x , Z, G) |
| Open Source Code | No | The paper does not include experiments requiring code. (Justification for question 5 in NeurIPS Paper Checklist) |
| Open Datasets | No | The paper does not include experiments. (Justification for question 4 in NeurIPS Paper Checklist) |
| Dataset Splits | No | The paper does not include experiments. (Justification for question 6 in NeurIPS Paper Checklist) |
| Hardware Specification | No | The paper does not include experiments. (Justification for question 8 in NeurIPS Paper Checklist) |
| Software Dependencies | No | The paper does not include experiments. (Justification for question 4 in NeurIPS Paper Checklist) |
| Experiment Setup | No | The paper does not include experiments. (Justification for question 6 in NeurIPS Paper Checklist) |