An auditable consensus workflow around four causal-discovery methods gives higher-precision links on synthetic benchmarks, but the precision gain disappears on a real river network where the reference graph is incomplete.
Springer, Berlin, Heidelberg
1 Pith paper cite this work, alongside 855 external citations. Polarity classification is still indexing.
1
Pith paper citing it
855
external citations · OpenAlex
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery
An auditable consensus workflow around four causal-discovery methods gives higher-precision links on synthetic benchmarks, but the precision gain disappears on a real river network where the reference graph is incomplete.