Learning of Causal Structures with Deep Reinforcement Learning

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Amirinezhad, Amir | 2020

1019 Viewed

Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 53480 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Saleh Kaleybar, Saber; Hashemi, Matin
Abstract:
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-learning 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
Keywords:
Reinforcement Learning ; Active Learning ; Causal Structure ; Experimental Design ; Directed Acyclic Graph (DAG) ; Causal Structures Learning ; Graph Neural Network