Enes Causal Discovery

Alexis Kafantaris

cs.NEMar 25, 2026

Enes Causal Discovery

Authors: Alexis Kafantaris

Abstract

Enes The proposed architecture is a mixture of experts, which allows for the model entities, such as the causal relationships, to be further parameterized. More specifically, an attempt is made to exploit a neural net as implementing neurons poses a great challenge for this dataset. To explain, a simple and fast Pearson coefficient linear model usually achieves good scores. An aggressive baseline that requires a really good model to overcome that is. Moreover, there are major limitations when it comes to causal discovery of observational data. Unlike the sachs one did not use interventions but only prior knowledge; the most prohibiting limitation is that of the data which is addressed. Thereafter, the method and the model are described and after that the results are presented.

Figures & tables

Panel of Figure 1 — Figure 1: Final architecture of the Enes Model. The model is comprised of a dual expert architecture and graph metrics that are treated as the physics of the network. It inputs graph nodes and outputs causal pattern classification.

Causal Discovery

Causal discovery infers cause-and-effect structures from observational and interventional data, distinguishing causal relationships from correlation.

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Arrow: A Foundation Model for Causal Discovery

Ryan Thompson, He Zhao, Daniel M. Steinberg, Edwin V. Bonilla

University of Technology Sydney · CSIRO’s Data61

Causal Foundation Models Tabular Foundation Models Causal Discovery

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We introduce Arrow, a foundation model for zero-shot causal discovery on observational tabular data. Arrow factorizes a directed acyclic graph into an undirected skeleton and a topological order, guaranteeing acyclicity by construction. Given a new dataset, it uses a transformer-based architecture to contextualize variables within and across observations, then predicts skeleton edge probabilities and node order scores that together define a graph. Arrow is trained in a supervised fashion on synthetic datasets with ground-truth graphs, using an end-to-end differentiable directed edge composite likelihood induced by the skeleton-order factorization. The training distribution spans diverse graph families, functional forms, noise models, and dataset shapes. Across in- and out-of-distribution synthetic, semi-synthetic, and real datasets, Arrow matches or outperforms existing causal discovery methods at substantially lower inference cost than competitive alternatives. Our results demonstrate that large-scale pretraining on diverse synthetic data can yield zero-shot causal discovery models that are fast, accurate, and reusable on new datasets.

Abstract

Causal Discovery

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