The Computational Complexity of Structure-Based Causality

Authors: Gadi Aleksandrowicz, Hana Chockler, Joseph Y. Halpern, Alexander Ivrii

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Reproducibility Variable Result LLM Response
Research Type Theoretical We show that the complexity of computing causality under the updated definition is DP 2 -complete. Chockler and Halpern extended the definition of causality by introducing notions of responsibility and blame, and characterized the complexity of determining the degree of responsibility and blame using the original definition of causality. Here, we completely characterize the complexity using the updated definition of causality.
Researcher Affiliation Collaboration Gadi Aleksandrowicz EMAIL IBM Research Lab Haifa, Israel Hana Chockler EMAIL Department of Informatics King s College London, UK Joseph Y. Halpern EMAIL Computer Science Department Cornell University, Ithaca, NY 14853, USA Alexander Ivrii EMAIL IBM Research Lab Haifa, Israel
Pseudocode No The paper describes algorithms and reductions in paragraph form and logical notation, but does not contain any structured pseudocode or algorithm blocks. For example, the proofs in Appendix A and B describe construction steps but not as formal algorithms.
Open Source Code No The paper does not contain any explicit statements about releasing source code for the methodology described, nor does it provide links to any code repositories.
Open Datasets No The paper is theoretical and focuses on computational complexity. It does not use or refer to any empirical datasets that would require public access information. Mentions of "binary models" and "general models" refer to types of causal models being analyzed, not specific data.
Dataset Splits No The paper is theoretical and does not involve experiments with datasets, therefore, there is no mention of training/test/validation dataset splits.
Hardware Specification No The paper is theoretical and focuses on computational complexity. It does not describe any experiments that would require specific hardware for execution, and thus no hardware specifications are provided.
Software Dependencies No The paper is theoretical and focuses on computational complexity. It does not describe any experimental setup or implementation details that would require specific software dependencies with version numbers.
Experiment Setup No The paper is theoretical and presents proofs and complexity analysis rather than empirical experiments. Therefore, there are no experimental setup details, hyperparameters, or training configurations mentioned.