  It is a long-standing question to discover causal relations among a set of
variables in many empirical sciences. Recently, Reinforcement Learning (RL) has
achieved promising results in causal discovery from observational data.
However, searching the space of directed graphs and enforcing acyclicity by
implicit penalties tend to be inefficient and restrict the existing RL-based
method to small scale problems. In this work, we propose a novel RL-based
approach for causal discovery, by incorporating RL into the ordering-based
paradigm. Specifically, we formulate the ordering search problem as a
multi-step Markov decision process, implement the ordering generating process
with an encoder-decoder architecture, and finally use RL to optimize the
proposed model based on the reward mechanisms designed for~each ordering. A
generated ordering would then be processed using variable selection to obtain
the final causal graph. We analyze the consistency and computational complexity
of the proposed method, and empirically show that a pretrained model can be
exploited to accelerate training. Experimental results on both synthetic and
real data sets shows that the proposed method achieves a much improved
performance over existing RL-based method.
