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Author |
Bertossi, L. |
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Title |
Specifying and computing causes for query answers in databases via database repairs and repair-programs |
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Year |
2021 |
Publication |
Knowledge And Information Systems |
Abbreviated Journal |
Knowl. Inf. Syst. |
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Volume |
63 |
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Pages |
199–231 |
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Keywords |
Causality; Databases; Repairs; Constraints; Answer-set programming |
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Abstract |
There is a recently established correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints. In this work, answer-set programs that specify database repairs are used as a basis for solving computational and reasoning problems around causality in databases, including causal responsibility. Furthermore, causes are introduced also at the attribute level by appealing to an attribute-based repair semantics that uses null values. Corresponding repair-programs are introduced, and used as a basis for computation and reasoning about attribute-level causes. The answer-set programs are extended in order to capture causality under integrity constraints. |
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Address |
[Bertossi, Leopoldo] Adolfo Ibanez Univ, Fac Engn & Sci, Santiago, Chile, Email: leopoldo.bertossi@uai.cl |
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Springer London Ltd |
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English |
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0219-1377 |
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WOS:000584967200001 |
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UAI @ alexi.delcanto @ |
Serial |
1258 |
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Author |
Bertossi, L. |
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Title |
Declarative Approaches to Counterfactual Explanations for Classification |
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Year |
2022 |
Publication |
Theory and Practice of Logic Programming |
Abbreviated Journal |
Theory Pract. Log. Program. |
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Early Access |
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Keywords |
classification; explanations; counterfactuals; causality; answer-set programming; constraints |
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Abstract |
We propose answer-set programs that specify and compute counterfactual interventions on entities that are input on a classification model. In relation to the outcome of the model, the resulting counterfactual entities serve as a basis for the definition and computation of causality-based explanation scores for the feature values in the entity under classification, namely responsibility scores. The approach and the programs can be applied with black-box models, and also with models that can be specified as logic programs, such as rule-based classifiers. The main focus of this study is on the specification and computation of best counterfactual entities, that is, those that lead to maximum responsibility scores. From them one can read off the explanations as maximum responsibility feature values in the original entity. We also extend the programs to bring into the picture semantic or domain knowledge. We show how the approach could be extended by means of probabilistic methods, and how the underlying probability distributions could be modified through the use of constraints. Several examples of programs written in the syntax of the DLV ASP-solver, and run with it, are shown. |
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1471-0684 |
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WOS:000734688600001 |
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UAI @ alexi.delcanto @ |
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1515 |
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