Active Learning of Causal Structures with Deep Reinforcement Learning
This addresses the challenge of efficient causal discovery for researchers in fields like biology or economics, though it is incremental as it builds on existing active learning methods with a new computational approach.
The paper tackles the problem of learning causal structures from interventional data by designing experiments to minimize the number of interventions needed, presenting a deep reinforcement learning solution that achieves competitive performance in recovery while significantly reducing execution time in dense graphs.
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.