Causal questions inquire about causal relationships between different events or phenomena. They are important for a variety of use cases, including virtual assistants and search engines. However, many current approaches to causal question answering cannot provide explanations or evidence for their answers. Hence, in this paper, we aim to answer causal questions with a causality graph, a large-scale dataset of causal relations between noun phrases along with the relations' provenance data. Inspired by recent, successful applications of reinforcement learning to knowledge graph tasks, such as link prediction and fact-checking, we explore the application of reinforcement learning on a causality graph for causal question answering. We introduce an Actor-Critic-based agent which learns to search through the graph to answer causal questions. We bootstrap the agent with a supervised learning procedure to deal with large action spaces and sparse rewards. Our evaluation shows that the agent successfully prunes the search space to answer binary causal questions by visiting less than 30 nodes per question compared to over 3,000 nodes by a naive breadth-first search. Our ablation study indicates that our supervised learning strategy provides a strong foundation upon which our reinforcement learning agent improves. The paths returned by our agent explain the mechanisms by which a cause produces an effect. Moreover, for each edge on a path, our causality graph provides its original source allowing for easy verification of paths.

翻译：摘要：因果问题探究不同事件或现象之间的因果关系，对于虚拟助手和搜索引擎等多种应用场景至关重要。然而，当前许多因果问答方法无法为答案提供解释或证据。为此，本文旨在利用因果图来回答因果问题，该图是一个大规模数据集，包含名词短语间的因果关系及其来源数据。受近期强化学习在知识图谱任务（如链接预测与事实核查）中成功应用的启发，我们探索了在因果图上应用强化学习进行因果问答的方法。我们提出一个基于Actor-Critic的智能体，通过学习在图中搜索以回答因果问题。为应对大动作空间与稀疏奖励问题，我们采用监督学习流程对智能体进行预训练。评估表明，该智能体可有效剪枝搜索空间：回答二元因果问题时，每个问题仅需访问不到30个节点，而朴素广度优先搜索需访问超过3000个节点。我们的消融实验表明，监督学习策略为强化学习智能体的改进奠定了坚实基础。智能体返回的路径解释了原因产生结果的机制。此外，对于路径上的每条边，因果图均提供其原始来源，便于路径验证。