数据管理中的因果效应分数

基于因果性的解释数据管理中的评分对齐

Causality-Based Scores Alignment in Explainable Data Management

本文研究了数据库查询中因果评分的对齐问题，发现外生元组的存在对评分一致性有关键影响。

Data Management for Causal Algorithmic Fairness

Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflects discrimination, suggesting a data management problem. In this paper, we first make a distinction between associational and causal definitions of fairness in the literature and argue that the concept of fairness requires causal reasoning. We then review existing works and identify future opportunities for applying data management techniques to causal algorithmic fairness.

Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence

In this expository article we highlight the relevance of explanations for artificial intelligence, in general, and for the newer developments in {\em explainable AI}, referring to origins and connections of and among different approaches. We describe in simple terms, explanations in data management and machine learning that are based on attribution-scores, and counterfactuals as found in the area of causality. We elaborate on the importance of logical reasoning when dealing with counterfactuals, and their use for score computation.

Score-Based Explanations in Data Management and Machine Learning

We describe some approaches to explanations for observed outcomes in data management and machine learning. They are based on the assignment of numerical scores to predefined and potentially relevant inputs. More specifically, we consider explanations for query answers in databases, and for results from classification models. The described approaches are mostly of a causal and counterfactual nature. We argue for the need to bring domain and semantic knowledge into score computations; and suggest some ways to do this.

数据管理与可解释机器学习中的归因分数

Attribution-Scores in Data Management and Explainable Machine Learning

本文介绍了实际因果性中的责任分数在数据库查询答案解释和机器学习分类模型输出解释中的应用，说明了与数据库修复的联系，扩展了分类模型的责任分数，并讨论了Shap-score的高效计算。

从数据库修复到数据库及其他领域中的因果关系

From Database Repairs to Causality in Databases and Beyond

该研究介绍了作者及合作者提出的数据库查询结果基于评分的解释方法，重点关注反事实推理在评分设定与计算中的应用，通过示例展现了方法的灵活性。