融合观测与干预数据，提升时间序列因果发现准确性
CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Series
- 结合观测与干预数据构建因果模型
- 在机器人操控基准上验证，显著提升因果推断准确率
- 适用于复杂系统如机器人控制，支持真实场景应用
因果关系研究在众多科学领域及智能系统应用中至关重要。当存在隐藏变量时，仅依赖观测数据的方法难以准确识别因果关系。本文提出CAnDOIT，一种利用观测与干预时间序列数据重构因果模型的方法。该方法在随机生成的合成模型和机器人操作环境中的经典因果结构学习基准上进行了验证。实验表明，该方法能有效处理干预数据，并利用其提升因果分析的准确性。CAnDOIT的Python实现已开源，可在GitHub获取：https://github.com/lcastri/causalflow。
原文摘要 · Abstract (English)
The study of cause-and-effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a major challenge for methods that rely solely on observational data for building causal models. This paper proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time-series data. The use of interventional data in the causal analysis is crucial for real-world applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well-known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT has also been developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.
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