Causal discovery is a major task with the utmost importance for machine learning since causal structures can enable models to go beyond pure correlation-based inference and significantly boost their performance. However, finding causal structures from data poses a significant challenge both in computational effort and accuracy, let alone its impossibility without interventions in general. In this paper, we develop a meta-reinforcement learning algorithm that performs causal discovery by learning to perform interventions such that it can construct an explicit causal graph. Apart from being useful for possible downstream applications, the estimated causal graph also provides an explanation for the data-generating process. In this article, we show that our algorithm estimates a good graph compared to the SOTA approaches, even in environments whose underlying causal structure is previously unseen. Further, we make an ablation study that shows how learning interventions contribute to the overall performance of our approach. We conclude that interventions indeed help boost the performance, efficiently yielding an accurate estimate of the causal structure of a possibly unseen environment.

翻译：因果发现是机器学习中最重要的一项任务，因为因果结构能够使模型超越基于纯相关性的推断，显著提升其性能。然而，从数据中寻找因果结构在计算效率和准确性方面均面临重大挑战，更不用说在无干预条件下这一任务普遍存在的不可能性。本文提出了一种元强化学习算法，通过学会执行干预操作来构建显式因果图，从而实现因果发现。除了可应用于下游任务外，所估计的因果图还能为数据生成过程提供解释。研究表明，与现有最优方法相比，即便在底层因果结构未见过的环境中，该算法仍能估计出高质量的图结构。此外，通过消融实验揭示了学习干预操作对算法整体性能的贡献。我们得出结论：干预确实有助于提升性能，从而高效地获得可能未知环境的准确因果结构估计。