Shapley Value

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Period ending 2026-09-21

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CausalLoss-Fin: Attributing Financial-Agent Loss to Decisions and Infrastructure Faults
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Evaluating Explanation Methods by the Predictors They Induce
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Shapley Value Estimation for Multi-Site Data with Blockwise-Missing Features
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A Computationally Feasible Framework for Causal Probabilistic Explanation
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SkillShapley: Boundary-Adaptive Shapley Valuation for Skill Step Attribution in LLM Agents
Aug 13, 2026Chang Liu, Yuqi Zhang, Yiman Zhong +3Agent Skill Ecosystems Large Language Model Agents
CAS: A Causal Attribution Score for Local and Global Explainable Artificial Intelligence
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RelShap: Relationally Consistent Shapley Explanations
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Interpretable machine learning for predicting splitting strength of asphalt concrete: insights from SHAP analysis
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Shapley-Value-Based Feature Attribution for Data Masking
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Global Sensitive-Based Input Shaping for UAV-Payload Precision Motion Control
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Jul 23, 2026Bizu Feng, Zhimu Yang, Shuming Wang +4Graph Neural Networks Shapley Value
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Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling
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Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring
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Exact and Certified Data Shapley for Weighted k-Nearest-Neighbor Regression and Soft-Label Prediction
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Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation
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Decision-Value Attribution in Predict-then-Optimize Systems
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An approach with Visual and Tabular Mamba to multimodal medical data using Mixed Fusion
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Sep 22, 2026 · cs.AIJ/K move · Enter open · S save

CausalLoss-Fin: Attributing Financial-Agent Loss to Decisions and Infrastructure Faults

Abhishek Sharma

When an agent handling a payment exception loses money, the agent-step attribution methods this paper compares against will name one of its actions. They will do so even when a settlement message was dropped and the agent never had a chance: they intervene on agent actions and do not expose infrastructure faults as intervenable variables, so every dollar they explain is charged to a decision. We take a benchmark whose fault process is explicit and replayable, decompose each episode's realised delivery schedule into named, individually repairable messages, and intervene on both the agent's choices and the infrastructure's. A telescoping identity splits any policy's loss exactly three ways: an infrastructure effect, a policy differential against the best implementable policy, and a reference-policy residual. Two of the three can be negative, so none is a share; Shapley then divides the first into signed allocations over individual messages. One result is structural and needs no corpus: an agent-only baseline identifies no infrastructure cause, because its model contains no variable that could name one. What 545 planted episodes across 3 policies measure is the size of that consequence. It misfiles 100% of infrastructure episodes and charges $114,383.40 to the agent. Repairing what it names recovers 0.0% of the available loss; repairing a minimal sufficient set recovers 100.0%. Scoring messages one at a time is not merely imprecise: 27.8% (95% CI: 23.3--32.3%) of episodes do not decompose additively. We evaluate deterministic programmatic policies rather than language-model agents, which is what makes replay exact and which limits external validity to stochastic agents. The prevalence figures are properties of this generator, not field rates.

Agent Decisions Shapley Value Credit Assignment

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