An automated LLM pipeline finds large differences in how well 11 real-world causal discovery benchmarks align with recent domain literature.
Bivariate causal discovery using Bayesian model selection.arXiv preprint arXiv:2306.02931
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
RDMDL estimates cause-variable complexity using minimum rate for histogram-derived distortion via information dimension, combined with standard mechanism complexity, yielding competitive causal direction accuracy on Tübingen data.
citing papers explorer
-
Consistency evaluation of benchmarks used for causal discovery
An automated LLM pipeline finds large differences in how well 11 real-world causal discovery benchmarks align with recent domain literature.
-
Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approach
RDMDL estimates cause-variable complexity using minimum rate for histogram-derived distortion via information dimension, combined with standard mechanism complexity, yielding competitive causal direction accuracy on Tübingen data.