Enes Causal Discovery

1. 1Implement a mixture of experts architecture to model causal relationships
2. 2Use a neural network to parameterize the model entities
3. 3Compare the performance of the proposed model with a baseline Pearson coefficient linear model
4. 4Evaluate the results to determine the effectiveness of the proposed architecture

Data scientists and AI engineers on a team can benefit from this research to improve their understanding of causal relationships in complex datasets, and apply it to their machine learning models

💡 The proposed architecture allows for further parameterization of model entities, such as causal relationships, using a neural network

💡 Enes Causal Discovery: a new architecture for modeling causal relationships using a mixture of experts #AI #causality