Abstract not found

An answer to a query has a well-defined lineage expression (alternatively called how-provenance) that explains how the answer was derived. Recent work has also shown how to compute the lineage of a non-answer to a query. However, the cause of an answer or non-answer is a more subtle notion and consists, in general, of only a fragment of the lineage. In this paper, we adapt Halpern, Pearl, and Chockler’s recent definitions of causality and responsibility to define the causes of answers and non-answers to queries, and their degree of responsibility. Responsibility captures the notion of degree of causality and serves to rank potentially many causes by their relative contributions to the effect. Then, we study the complexity of computing causes and responsibilities for conjunctive queries. It is known that computing causes is NP-complete in general. Our first main result shows that all causes to conjunctive queries can be computed by a relational query which may involve negation. Thus, causality can be computed in PTIME, and very efficiently so. Next, we study computing responsibility. Here, we prove that the complexity depends on the conjunctive query and demonstrate a dichotomy between PTIME and NP-complete cases. For the PTIME cases, we give a non-trivial algorithm, consisting of a reduction to the max-flow computation problem. Finally, we prove that, even when it is in PTIME, responsibility is complete for LOGSPACE, implying that, unlike causality, it cannot be computed by a relational query. 1.

This paper is directed towards combining Pearl’s structural-model approach to causal reasoning with high-level formalisms for reasoning about actions. More precisely, we present a combination of Pearl’s structural-model approach with Poole’s independent choice logic. We show how probabilistic theories in the independent choice logic can be mapped to probabilistic causal models. This mapping provides the independent choice logic with appealing concepts of causality and explanation from the structural-model approach. We illustrate this along Halpern and Pearl’s sophisticated notions of actual cause, explanation, and partial explanation. Furthermore, this mapping also adds first-order modeling capabilities and explicit actions to the structural-model approach.

Even when a system is proven to be correct with respect to a specification, there is still a question of how complete the specification is, and whether it really covers all the behaviors of the system. Coverage metrics attempt to check which parts of a system are actually relevant for the verification process to succeed. Recent work on coverage in model checking suggests several coverage metrics and algorithms for finding parts of the system that are not covered by the specification. The work has already proven to be effective in practice, detecting design errors that escape early verification efforts in industrial settings. In this paper, we relate a formal definition of causality given by Halpern and Pearl [2005] to coverage. We show that it gives significant insight into unresolved issues regarding the definition of coverage and leads to potentially useful extensions of coverage. In particular, we introduce the notion of responsibility, which assigns to components of a system a quantitative measure of their relevance to the satisfaction of the specification. 1