Affiliation:
1. University of Zurich, Zurich, Switzerland
2. University of Washington, Seattle, WA, USA
Abstract
In this paper we investigate the problem of quantifying the contribution of each variable to the satisfying assignments of a Boolean function based on the Shapley value. Our main result is a polynomial-time equivalence between computing Shapley values and model counting for any class of Boolean functions that are closed under substitutions of variables with disjunctions of fresh variables. This result settles an open problem raised in prior work, which sought to connect the Shapley value computation to probabilistic query evaluation.
We show two applications of our result. First, the Shapley values can be computed in polynomial time over deterministic and decomposable circuits, since they are closed under OR-substitutions. Second, there is a polynomial-time equivalence between computing the Shapley value for the tuples contributing to the answer of a Boolean conjunctive query and counting the models in the lineage of the query. This equivalence allows us to immediately recover the dichotomy for Shapley value computation in case of self-join-free Boolean conjunctive queries; in particular, the hardness for non-hierarchical queries can now be shown using a simple reduction from the \#P-hard problem of model counting for lineage in positive bipartite disjunctive normal form.
Funder
NSF IIS
UZH Global Strategy and Partnerships Funding Scheme
Simons Program on Logic and Algorithms in Databases and AI
NSF-BSF
Publisher
Association for Computing Machinery (ACM)
Reference33 articles.
1. Marcelo Arenas Pablo Barceló Leopoldo E. Bertossi and Mikaë l Monet. 2021. The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and Decomposable Boolean Circuits. In AAAI. 6670--6678.
2. On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results;Arenas Marcelo;J. Mach. Learn. Res.,2023
3. Marcelo Arenas Pablo Barceló Miguel A. Romero Orth and Bernardo Subercaseaux. 2022. On Computing Probabilistic Explanations for Decision Trees. In NeurIPS.
4. Conditional Dichotomy of Boolean Ordered Promise CSPs
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