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Active learning of causal structures with deep reinforcement learning

Amirinezhad, A ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1016/j.neunet.2022.06.028
  3. Publisher: Elsevier Ltd , 2022
  4. Abstract:
  5. We study the problem of experiment design to learn causal structures from interventional data. We consider an active learning setting in which the experimenter decides to intervene on one of the variables in the system in each step and uses the results of the intervention to recover further causal relationships among the variables. The goal is to fully identify the causal structures with minimum number of interventions. We present the first deep reinforcement learning based solution for the problem of experiment design. In the proposed method, we embed input graphs to vectors using a graph neural network and feed them to another neural network which outputs a variable for performing intervention in each step. Both networks are trained jointly via a Q-iteration algorithm. Experimental results show that the proposed method achieves competitive performance in recovering causal structures with respect to previous works, while significantly reducing execution time in dense graphs. © 2022
  6. Keywords:
  7. Active learning ; Deep learning ; Feedforward neural networks ; Graph neural networks ; Iterative methods ; Casual structure learning ; Causal relationships ; Deep reinforcement learning ; Experiment design ; Interventional ; Learn+ ; Learning settings ; Reinforcement learnings ; Structure-learning ; Reinforcement learning ; Algorithm ; Learning ; Reinforcement (psychology) ; Algorithms ; Neural Networks, Computer ; Reinforcement, Psychology
  8. Source: Neural Networks ; Volume 154 , 2022 , Pages 22-30 ; 08936080 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S089360802200243X