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Neural Network Parameter-optimization of Gaussian pmDAGs

Created by
  • Haebom

Author

Mehrzad Saremi

Outline

This paper presents a novel approach for parameter exploration of latent variable causal models. Specifically, we propose a graph structure that is stable against marginalization in Gaussian Bayesian networks and, for the first time, reveal the duality between parameter optimization of latent variable models and feed-forward neural network training. Building on this, we develop an algorithm that optimizes the parameters of the graph structure using observed distributions and provide conditions for the identifiability of causal effects in Gaussian settings. Furthermore, we propose a meta-algorithm for verifying the identifiability of causal effects, laying the foundation for generalizing the duality between neural networks and causal models beyond the Gaussian distribution to other distributions.

Takeaways, Limitations

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Takeaways:
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A novel algorithm for parameter optimization of latent variable causal models is presented.
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Providing a new perspective and methodology for causal inference research
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Development of a meta-algorithm to verify the possibility of identifying causal effects.
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Discovering the duality between neural networks and causal models and suggesting generalizability.
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Limitations:
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Focusing on the Gaussian distribution, generalization to other distributions is still in its infancy.
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Further experiments and analysis are needed to determine the actual performance and efficiency of the algorithm.
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Further research is needed on the complexity and computational cost of the proposed method.
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