Showing cs.LGShow all
3 papers · 1 filter
cs.LG2024
Using LLMs for Explaining Sets of Counterfactual Examples to Final Users
Arturo Fredes, Jordi Vitria
Causality is vital for understanding true cause-and-effect relationships between variables within predictive models, rather than relying on mere correlations, making it highly rele…
cs.LG2024
Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept
Alexandre Trilla, Rajesh Rajendran, Ossee Yiboe +3
This paper describes the development of a counterfactual Root Cause Analysis diagnosis approach for an industrial multivariate time series environment. It drives the attention towa…
cs.LG2024
Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation
Roger Pros, Jordi Vitrià
In recent years, there has been a growing interest in using machine learning techniques for the estimation of treatment effects. Most of the best-performing methods rely on represe…