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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
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Active learning of causal structures with deep reinforcement learning.
Amir Amirinezhad1, Saber Salehkaleybar1, Matin Hashemi1
1Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
Summary
This study introduces a novel deep reinforcement learning approach for experiment design to discover causal structures. The method efficiently identifies relationships using minimal interventions, outperforming prior techniques in speed for complex systems.
Area of Science:
- Causal Inference and Machine Learning
- Computational Biology and Systems Biology
Background:
- Learning causal structures from interventional data is crucial for understanding complex systems.
- Traditional experiment design methods can be inefficient, requiring numerous interventions.
Purpose of the Study:
- To develop an automated experiment design strategy for efficient causal structure discovery.
- To minimize the number of interventions required for full causal structure identification.
Main Methods:
- A deep reinforcement learning framework is proposed for active learning in experiment design.
- Graph neural networks embed system graphs, and a Q-iteration algorithm trains intervention selection.
- The model learns to select optimal variables for intervention to infer causal relationships.
Main Results:
- The proposed deep reinforcement learning method achieves competitive performance in recovering causal structures.
- Significant reduction in execution time, particularly for dense graphs, compared to previous works.
- Demonstrates the efficacy of automated experiment design in accelerating causal discovery.
Conclusions:
- Deep reinforcement learning offers a powerful and efficient solution for experiment design in causal discovery.
- The method provides a scalable approach for identifying complex causal relationships with minimal experimental effort.
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