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The Ethereal
Causality-based Explanation of Classification Outcomes
March 15, 2020 ยท Declared Dead ยท ๐ DEEM@SIGMOD
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Leopoldo Bertossi, Jordan Li, Maximilian Schleich, Dan Suciu, Zografoula Vagena
arXiv ID
2003.06868
Category
cs.LG: Machine Learning
Cross-listed
cs.AI,
cs.DB,
stat.ML
Citations
47
Venue
DEEM@SIGMOD
Last Checked
3 months ago
Abstract
We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality. We compare it with previously proposed notions of explanation, and study their complexity. We conduct an experimental evaluation with two real datasets from the financial domain.
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