Active Learning of Causal Structures with Deep Reinforcement Learning

TL;DR

This paper introduces a novel deep reinforcement learning approach for designing experiments to efficiently learn causal structures from interventional data, significantly reducing the number of interventions needed.

Contribution

It presents the first deep reinforcement learning method for causal experiment design, utilizing graph neural networks and joint training to improve efficiency and speed.

Findings

Achieves competitive accuracy in causal structure recovery.

Reduces execution time in dense graphs.

Outperforms previous methods in intervention efficiency.

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-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.