The Computational Complexity of Structure-Based Causality
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Abstract (arXiv)
Halpern and Pearl introduced a definition of actual causality; Eiter and Lukasiewicz showed that computing whether X=x is a cause of Y=y is NP-complete in binary models (where all variables can take on only two values) and Sigma2P-complete in general models. In the final version of their paper, Halpern and Pearl slightly modified the definition of actual cause, in order to deal with problems pointed by Hopkins and Pearl. As we show, this modification has a nontrivial impact on the complexity of computing actual cause. To characterize the complexity, a new family DkP, k= 1, 2, 3, ..., of complexity classes is introduced, which generalizes the class DP introduced by Papadimitriou and Yannakakis (DP is just D1P). %joe2 %We show that the complexity of computing causality is D2-complete %under the new definition. Chockler and Halpern citeyearCH04 extended the We show that the complexity of computing causality under the updated definition is D2P-complete. Chockler and Halpern extended the definition of causality by introducing notions of responsibility and blame. The complexity of determining the degree of responsibility and blame using the original definition of causality was completely characterized. Again, we show that changing the definition of causality affects the complexity, and completely characterize it using the updated definition.
Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). First posted on arXiv 9 Dec 2014.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 4; 2024: 1; 2025: 1; 2026: 5.
12 papers.
- 2023The Hardness of Reasoning about Probabilities and Causalitycited by 4
- 2024Automated Discovery of Functional Actual Causes in Complex Environmentscited by 4
- 2023Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPscited by 3
- 2021Equilibrium Design for Concurrent Gamescited by 2
- 2023Analyzing Intentional Behavior in Autonomous Agents under Uncertaintycited by 2
- 2023Counterfactuals Modulo Temporal Logicscited by 1
- 2025Searching for Actual Causes: Approximate Algorithms with Adjustable Precisioncited by 1
- 2026Binary Spiking Neural Networks as Causal Modelscited by 1
- 2026Actual causality in fault treescited by 0
- 2026Computing Actual Causes for Neural Network Predictions under Structured Causal Inputscited by 0
- 2026HANSARD: A Reference Architecture for Forensic Readiness, Runtime Witnessing, and Graded Attribution in Autonomous Multi-Agent AI Systemscited by 0
- 2026Certifying Interventional Agreement Among Observationally Equivalent Causal Modelscited by 0
- 2026Certifying Interventional Agreement Among Observationally Equivalent Causal Models26 Sep 2026
- 2026HANSARD: A Reference Architecture for Forensic Readiness, Runtime Witnessing, and Graded Attribution in Autonomous Multi-Agent AI Systems23 Aug 2026
- 2026Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs4 Aug 2026
- 2026Actual causality in fault trees2 Jul 2026
- 2026Binary Spiking Neural Networks as Causal Models29 Apr 2026
- 2025Searching for Actual Causes: Approximate Algorithms with Adjustable Precision10 Jul 2025
- 2024Automated Discovery of Functional Actual Causes in Complex Environments16 Apr 2024
- 2023Analyzing Intentional Behavior in Autonomous Agents under Uncertainty4 Jul 2023
- 2023Counterfactuals Modulo Temporal Logics15 Jun 2023
- 2023The Hardness of Reasoning about Probabilities and Causality16 May 2023
- 2023Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPs24 Feb 2023
- 2021Equilibrium Design for Concurrent Games18 Jun 2021