On the Computation of Necessary and Sufficient Explanations
Adnan Darwiche, Chunxi Ji
Abstract
The complete reason behind a decision is a Boolean formula that characterizes why the decision was made. This recently introduced notion has a number of applications, which include generating explanations, detecting decision bias and evaluating counterfactual queries. Prime implicants of the complete reason are known as sufficient reasons for the decision and they correspond to what is known as PI explanations and abductive explanations. In this paper, we refer to the prime implicates of a complete reason as necessary reasons for the decision. We justify this terminology semantically and show that necessary reasons correspond to what is known as contrastive explanations. We also study the computation of complete reasons for multi-class decision trees and graphs with nominal and numeric features for which we derive efficient, closed-form complete reasons. We further investigate the computation of shortest necessary and sufficient reasons for a broad class of complete reasons, which include the derived closed forms and the complete reasons for Sentential Decision Diagrams (SDDs). We provide an algorithm which can enumerate their shortest necessary reasons in output polynomial time. Enumerating shortest sufficient reasons for this class of complete reasons is hard even for a single reason. For this problem, we provide an algorithm that appears to be quite efficient as we show empirically.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 644a82df-e308-470d-a2d4-f0a2efe30481Cited by top-tier papers9
- Local vs. Global Interpretability: A Computational Complexity PerspectiveShahaf Bassan, Guy Amir, Guy KatzICML 2024 · 28 citations
- Solving Explainability Queries with Quantification: The Case of Feature RelevancyXuanxiang Huang, Yacine Izza, João Marques-SilvaAAAI 2023 · 16 citations
- Formal Mechanistic Interpretability: Automated Circuit Discovery with Provable GuaranteesItamar Hadad, Guy Katz, Shahaf BassanICLR 2026 · 10 citations
- SHAP Meets Tensor Networks: Provably Tractable Explanations with ParallelismReda Marzouk, Shahaf Bassan, Guy KatzNeurIPS 2025 · 9 citations
- FAME: Formal Abstract Minimal Explanation for Neural NetworksRyma Boumazouza, Raya Elsaleh, Melanie Ducoffe, Shahaf Bassan et al.ICLR 2026 · 6 citations
Builds on1
Related papers
- Trading Complexity for Sparsity in Random Forest ExplanationsGilles Audemard, Steve Bellart, Louenas Bounia, Frédéric Koriche et al.AAAI 2022 · 58 citations
- Tractable Explanations for d-DNNF ClassifiersXuanxiang Huang, Yacine Izza, Alexey Ignatiev, Martin C. Cooper et al.AAAI 2022 · 43 citations
- On Computing Probabilistic Explanations for Decision TreesMarcelo Arenas, Pablo Barceló, Miguel A. Romero Orth, Bernardo SubercaseauxNeurIPS 2022 · 57 citations
- Explaining Random Forests Using Bipolar Argumentation and Markov NetworksNico Potyka, Xiang Yin, Francesca ToniAAAI 2023 · 18 citations
- Unifying Formal Explanations: A Complexity-Theoretic PerspectiveShahaf Bassan, Xuanxiang Huang, Guy KatzICLR 2026 · 3 citations
