mlsanityData to 14 Oct 2026

Counterfactuals Modulo Temporal Logics

arXiv 2306.08916 ↗First posted 15 Jun 2023cs.LO cs.AIcited by 1

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Abstract (arXiv)

Lewis' theory of counterfactuals is the foundation of many contemporary notions of causality. In this paper, we extend this theory in the temporal direction to enable symbolic counterfactual reasoning on infinite sequences, such as counterexamples found by a model checker and trajectories produced by a reinforcement learning agent. In particular, our extension considers a more relaxed notion of similarity between worlds and proposes two additional counterfactual operators that close a semantic gap between the previous two in this more general setting. Further, we consider versions of counterfactuals that minimize the distance to the witnessing counterfactual worlds, a common requirement in causal analysis. To automate counterfactual reasoning in the temporal domain, we introduce a logic that combines temporal and counterfactual operators, and outline decision procedures for the satisfiability and trace-checking problems of this logic.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 49 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

17 of 49 references.

References · 17

  1. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  2. 2017Counterfactual Fairnesscited by 804
  3. 2016Reinforcement Learning With Temporal Logic Rewardscited by 117
  4. 2020A Composable Specification Language for Reinforcement Learning Taskscited by 69
  5. 2015A Modification of the Halpern-Pearl Definition of Causalitycited by 47
  6. 2014Temporal Logics for Hyperpropertiescited by 19
  7. 2022Causality in Configurable Software Systemscited by 13
  8. 2014The Computational Complexity of Structure-Based Causalitycited by 12
  9. 2023AutoHyper: Explicit-State Model Checking for HyperLTLcited by 8
  10. 2021Reasoning About Causal Models With Infinitely Many Variablescited by 7
  11. 2013Causes and Explanations: A Structural-Model Approach --- Part 1: Causescited by 6
  12. 2020Probabilistic Hyperproperties of Markov Decision Processescited by 6
5 more
  1. 2021Probabilistic causes in Markov chainscited by 4
  2. 2018HyperPCTL: A Temporal Logic for Probabilistic Hyperpropertiescited by 3
  3. 2020The Hierarchy of Hyperlogicscited by 3
  4. 2021Causality-Based Game Solvingcited by 3
  5. 2022Explaining Hyperproperty Violationscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guaranteescited by 0