13 citations · 29 across the 5 of their papers we have counts for
6 papers
On Explaining Confounding Bias
Brit Youngmann, Michael Cafarella, Yuval Moskovitch +1
When analyzing large datasets, analysts are often interested in the explanations for surprising or unexpected results produced by their queries. In this work, we focus on aggregate…
Interpretable Data-Based Explanations for Fairness Debugging
Romila Pradhan, Jiongli Zhu, Boris Glavic +1
A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches have been proposed in the literature to identify bias in machine learning models that ar…
ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data
Babak Salimi, Dan Suciu
Causal inference from observational data is a subject of active research and development in statistics and computer science. Many toolkits have been developed for this purpose that…
From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back
Babak Salimi, Leopoldo Bertossi
In this work we establish and investigate connections between causality for query answers in databases, database repairs wrt. denial constraints, and consistency-based diagnosis. T…
Unifying Causality, Diagnosis, Repairs and View-Updates in Databases
Leopoldo Bertossi, Babak Salimi
In this work we establish and point out connections between the notion of query-answer causality in databases and database repairs, model-based diagnosis in its consistency-based a…
Causality in Databases: The Diagnosis and Repair Connections
Babak Salimi, Leopoldo Bertossi
In this work we establish and investigate the connections between causality for query answers in databases, database repairs wrt. denial constraints, and consistency-based diagnosi…