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].
Counterfactual Graphical Models: Constraints and Inference
Authors: Juan D. Correa, Elias Bareinboim
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we introduce an efficient graphical construction called Ancestral Multi-world Networks that is sound and complete for reading counterfactual independences from a causal diagram using d-separation. Moreover, we introduce the counterfactual (ctf-) calculus, which can be used to transform counterfactual quantities using three rules licensed by the constraints encoded in the diagram. This result generalizes Pearl s celebrated do-calculus from interventional to counterfactual reasoning. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Universidad Autónoma de Manizales, Manizales, Colombia 2Department of Commputer Science, Columbia University, New York, United States. Correspondence to: Juan D. Correa <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 AMWN-CONSTRUCT(G, Y ) Input: Causal diagram G and a set of counterfactual variables Y . Output: GA(Y ) the AMWN of G and Y . |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code, a link to a code repository, or mention of code in supplementary materials. |
| Open Datasets | No | The paper describes theoretical constructs (Ancestral Multi-world Networks and counterfactual calculus) and uses causal diagrams for its examples. It does not mention the use of any specific real-world or publicly available datasets. |
| Dataset Splits | No | The paper does not utilize any datasets for experiments; therefore, it does not provide information regarding dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical contributions, such as graphical constructions and inference rules. It does not describe any experiments that would require specific hardware, and thus no hardware specifications are provided. |
| Software Dependencies | No | The paper focuses on theoretical methods and does not describe any experimental implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper presents theoretical work on counterfactual graphical models and calculus. It does not describe any empirical experiments, and therefore no experimental setup details, hyperparameters, or training configurations are provided. |