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Causal Relational Learning
A declarative language called CARL is proposed for capturing causal background knowledge and assumptions, and specifying causal queries using simple Datalog-like rules, which provides a foundation for inferring causality and reasoning about the effect of complex interventions in relational domains.
Demonstration of inferring causality from relational databases with CaRL
CaRL is proposed and demonstrated: an end-to-end system for drawing causal inference from relational data and a visual interface to wrap around CaRL is built.
Mining Robust Default Configurations for Resource-constrained AutoML
A novel method of selecting performant configurations for a given task by performing offline automl and mining over a diverse set of tasks, which demonstrates an improvement on the state-of-the-art by testing over 62 classification and regression datasets.