Counterfactual Graphical Models: Constraints and Inference

Authors: Juan D. Correa, Elias Bareinboim

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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.