Pretrained Causal Discovery at Any Size

Pretrained causal discovery from observational data trains a model on simulated systems and applies it to new data in a single inference pass. In the temporal case, most such models read the raw time series, tying them to the scale of the training data and restricting flexibility. A different strategy, which began with supervised cause-effect classification over features of a sample, turns discovery into supervised learning over features of the data rather than over the data itself (D2C), and and was recently extended to time series (TD2C). We present a deep learning reformulation of that strategy, proposing an architecture that works with a single tensor holding a family of statistics, at the level of every target, source and lag triplet. For this reason, its representation is not tied to a particular number of variables or lags, and when evaluated against its pairwise predecessor and against classical discovery algorithms, it matches or exceeds both in accuracy while scaling far better with the number of variables.