mlsanityData to 7 Oct 2026

Enes Causal Discovery

arXiv 2603.24436 ↗First posted 25 Mar 2026cs.NE cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 25 Mar 2026.

Builds on

5 of 17 references.

References · 5

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  2. 2021BASE Layers: Simplifying Training of Large, Sparse Modelscited by 315
  3. 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 99
  4. 2016Discovering Causal Signals in Imagescited by 79
  5. 2025Differentiable Constraint-Based Causal Discoverycited by 4

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026CauScale: Neural Causal Discovery at Scale
  2. 2026CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
  3. 2023Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
  4. 2025Can LLMs Leverage Observational Data? Towards Data-Driven Causal Discovery with LLMs
  5. 2024LLM-initialized Differentiable Causal Discovery
  6. 2025CARE: Turning LLMs Into Causal Reasoning Expert
  7. 2023Integrating Large Language Model for Improved Causal Discovery
  8. 2026Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting