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Gateways to Tractability for Satisfiability in Pearl's Causal Hierarchy

arXiv 2511.08091 ↗First posted 11 Nov 2025cs.AI cs.CCcited by 0

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Abstract (arXiv)

Pearl's Causal Hierarchy (PCH) is a central framework for reasoning about probabilistic, interventional, and counterfactual statements, yet the satisfiability problem for PCH formulas is computationally intractable in almost all classical settings. We revisit this challenge through the lens of parameterized complexity and identify the first gateways to tractability. Our results include fixed-parameter and XP-algorithms for satisfiability in key probabilistic and counterfactual fragments, using parameters such as primal treewidth and the number of variables, together with matching hardness results that map the limits of tractability. Technically, we depart from the dynamic programming paradigm typically employed for treewidth-based algorithms and instead exploit structural characterizations of well-formed causal models, providing a new algorithmic toolkit for causal reasoning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 45 references that are in the map. First posted on arXiv 11 Nov 2025.

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14 of 45 references.

References · 14

  1. 2021Partial Counterfactual Identification from Observational and Experimental Datacited by 64
  2. 2020Probabilistic Reasoning across the Causal Hierarchycited by 19
  3. 2020Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysiscited by 13
  4. 2014Parameterized Complexity Results for Exact Bayesian Network Structure Learningcited by 11
  5. 2021Is Causal Reasoning Harder than Probabilistic Reasoning?cited by 10
  6. 2021On the Parameterized Complexity of Polytree Learningcited by 6
  7. 2018The Complexity Landscape of Decompositional Parameters for ILPcited by 4
  8. 2023The Hardness of Reasoning about Probabilities and Causalitycited by 4
  9. 2024The Existential Theory of the Reals as a Complexity Class: A Compendiumcited by 4
  10. 2022Hedonic Games and Treewidth Revisitedcited by 3
  11. 2022Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderingscited by 3
  12. 2024From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchycited by 3
2 more
  1. 2023Conditional lower bounds for sparse parameterized 2-CSP: A streamlined proofcited by 1
  2. 2025Probabilistic and Causal Satisfiability: Constraining the Modelcited by 1

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  1. 2024On Probabilistic and Causal Reasoning with Summation Operators
  2. 2026Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification
  3. 2024The Existential Theory of the Reals with Summation Operators
  4. 2025A Non-Interventionist Approach to Causal Reasoning based on Lewisian Counterfactuals
  5. 2023Explaining SAT Solving Using Causal Reasoning
  6. 2022Markov categories, causal theories, and the do-calculus
  7. 2025A Fine-Grained Complexity View on Propositional Abduction -- Algorithms and Lower Bounds
  8. 2024Large Language Models for Constrained-Based Causal Discovery