论文标题
通过干预措施解决环境因果模型中的虚假相关性
Resolving Spurious Correlations in Causal Models of Environments via Interventions
论文作者
论文摘要
因果模型通过使其可解释，样本效率和强大的输入分布变化来为决策系统（或代理）带来许多好处。但是，虚假的相关性可能导致错误的因果模型和预测。我们考虑推断增强学习环境的因果模型的问题，并提出了一种处理虚假相关性的方法。具体而言，我们的方法设计了一种奖励功能，该奖励功能激励代理商进行干预以查找因果模型中的错误。通过进行干预获得的数据用于改善因果模型。我们提出了几种干预设计方法并进行比较。在网格世界环境中的实验结果表明，与基线相比，我们的方法会导致更好的因果模型：从随机策略或对环境奖励培训的策略中学习模型。主要贡献包括设计干预措施以解决虚假相关性的方法。
Causal models bring many benefits to decision-making systems (or agents) by making them interpretable, sample-efficient, and robust to changes in the input distribution. However, spurious correlations can lead to wrong causal models and predictions. We consider the problem of inferring a causal model of a reinforcement learning environment and we propose a method to deal with spurious correlations. Specifically, our method designs a reward function that incentivizes an agent to do an intervention to find errors in the causal model. The data obtained from doing the intervention is used to improve the causal model. We propose several intervention design methods and compare them. The experimental results in a grid-world environment show that our approach leads to better causal models compared to baselines: learning the model on data from a random policy or a policy trained on the environment's reward. The main contribution consists of methods to design interventions to resolve spurious correlations.
