Causal discovery now reads direction from a symbolic-regression posterior: causalBMS tops 13 of 21 CauseMe dataset-metric pairs and returns closed-form mechanisms
Causal discovery no longer has to rest on conditional-independence tests or a single best-scoring model. causalBMS scores each candidate parent by how often it appears in expressions sampled from a Bayesian symbolic-regression posterior, so it still gives a direction in bivariate, strongly autocorrelated and high-noise settings. On the CauseMe benchmark it ranked first in 13 of 21 dataset-metric pairs and second in four more, while also returning closed-form functional forms.