Enes Causal Discovery

TL;DR

This paper introduces a mixture of experts architecture for causal discovery, addressing limitations of observational data and leveraging neural networks to model causal relationships.

Contribution

It proposes a novel mixture of experts model for causal discovery that incorporates neural networks and handles observational data limitations.

Findings

The model achieves good scores with simple linear models on certain datasets.

The approach addresses data limitations in causal discovery without interventions.

Results demonstrate the effectiveness of the proposed architecture.

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.