From Verification to Causality-Based Explications (Invited Talk)

Baier, Christel; Dubslaff, Clemens; Funke, Florian; Jantsch, Simon; Majumdar, Rupak; Piribauer, Jakob; Ziemek, Robin

doi:10.4230/lipics.icalp.2021.1

Cited by 7 publications

(7 citation statements)

References 87 publications

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“…Causal analysis of hyperproperties has been studied theoretically based on counterfactual builders [40] instead of actual causality, as in our work. Explanation methods [4] exist for trace properties [5,39,41,42,70], integrated in several model checkers [14,15,19]. Minimization [54] has been studied, as well as analyzing several system traces together [9,43,65].…”

Section: Related Workmentioning

confidence: 99%

Explaining Hyperproperty Violations

Coenen

Dachselt

Finkbeiner

et al. 2022

Lecture Notes in Computer Science

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Hyperproperties relate multiple computation traces to each other. Model checkers for hyperproperties thus return, in case a system model violates the specification, a set of traces as a counterexample. Fixing the erroneous relations between traces in the system that led to the counterexample is a difficult manual effort that highly benefits from additional explanations. In this paper, we present an explanation method for counterexamples to hyperproperties described in the specification logic HyperLTL. We extend Halpern and Pearl’s definition of actual causality to sets of traces witnessing the violation of a HyperLTL formula, which allows us to identify the events that caused the violation. We report on the implementation of our method and show that it significantly improves on previous approaches for analyzing counterexamples returned by HyperLTL model checkers.

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Section: Related Workmentioning

confidence: 99%

Explaining Hyperproperty Violations

Coenen

Dachselt

Finkbeiner

et al. 2022

Lecture Notes in Computer Science

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“…Kleinberg [23,24] introduced prima facie causes in finite Markov chains where both causes and effects are formalized as PCTL state formulae, and thus they can be seen as sets of states as in our approach. The correspondence of Kleinberg's PCTL constraints for prima facie causes and the strict probability-raising condition formalized using conditional probabilities has been worked out in the survey article [5]. Our notion of SPR causes corresponds to Kleinberg's prima facie causes, except for the minimality condition (M).…”

Section: Related Workmentioning

confidence: 99%

“…The survey article [5] introduces notions of global probability-raising causes for Markov chains where causes and effects can be path properties. [5]'s notion of reachability causes in Markov chains directly corresponds to our notion GPR causes, the only difference being that [5] deals with a relaxed minimality condition and requires that the cause set is reachable without visiting an effect state before. The latter is inherent in our approach as we suppose that all states are reachable and the effect states are terminal.…”

Section: Related Workmentioning

confidence: 99%

“…) from Section 4.2 in time polynomial in the size of the original MDP and the size of the representation of the solution to (1)-(5).D Omitted Proofs and Details of Section Proofs of Section 5.2The following lemma shows that all three quality measures are preserved by the switch from M to M [Cause] .Lemma 17. If Cause is an SPR or a GPR cause then:recall M (Cause) = recall M [Cause] (Cause) covrat M (Cause) = covrat M [Cause] (Cause) fscore M (Cause) = fscore M [Cause] (Cause)Proof.…”

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confidence: 99%

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On probability-raising causality in Markov decision processes

Baier¹,

Funke²,

Piribauer³

et al. 2022

Preprint

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The purpose of this paper is to introduce a notion of causality in Markov decision processes based on the probability-raising principle and to analyze its algorithmic properties. The latter includes algorithms for checking causeeffect relationships and the existence of probability-raising causes for given effect scenarios. Inspired by concepts of statistical analysis, we study quality measures (recall, coverage ratio and f-score) for causes and develop algorithms for their computation. Finally, the computational complexity for finding optimal causes with respect to these measures is analyzed.

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“…To provide additional information on why the system behaves as it does, certificates witnessing the result of the model-checking procedure, in particular counterexample traces in case of a negative result, have been studied extensively (see, e.g., [10,29,9,30]). Due to the potentially still enormous size of counterexample traces and other certificates, a line of research has emerged that tries to distill comprehensible explications of what causes the system to behave as it does using formalizations of causality (see, e.g., [32,33,2]).…”

Section: Introductionmentioning

confidence: 99%

Counterfactual Causality for Reachability and Safety based on Distance Functions

Parreaux,

Piribauer,

Baier

2023

Electron. Proc. Theor. Comput. Sci.

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Investigations of causality in operational systems aim at providing human-understandable explanations of why a system behaves as it does. There is, in particular, a demand to explain what went wrong on a given counterexample execution that shows that a system does not satisfy a given specification. To this end, this paper investigates a notion of counterfactual causality in transition systems based on Stalnaker's and Lewis' semantics of counterfactuals in terms of most similar possible worlds and introduces a novel corresponding notion of counterfactual causality in two-player games. Using distance functions between paths in transition systems to capture the similarity of executions, this notion defines whether reaching a certain set of states is a cause for the fact that a given execution of a system satisfies an undesirable reachability or safety property. Similarly, using distance functions between memoryless strategies in reachability and safety games, it is defined whether reaching a set of states is a cause for the fact that a given strategy for the player under investigation is losing.The contribution of the paper is two-fold: In transition systems, it is shown that counterfactual causality can be checked in polynomial time for three prominent distance functions between paths. In two-player games, the introduced notion of counterfactual causality is shown to be checkable in polynomial time for two natural distance functions between memoryless strategies. Further, a notion of explanation that can be extracted from a counterfactual cause and that pinpoints changes to be made to the given strategy in order to transform it into a winning strategy is defined. For the two distance functions under consideration, the problem to decide whether such an explanation imposes only minimal necessary changes to the given strategy with respect to the used distance function turns out to be coNP-complete and not to be solvable in polynomial time if P is not equal to NP, respectively.

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From Verification to Causality-Based Explications (Invited Talk)

Cited by 7 publications

References 87 publications

Explaining Hyperproperty Violations

Explaining Hyperproperty Violations

On probability-raising causality in Markov decision processes

Counterfactual Causality for Reachability and Safety based on Distance Functions

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