神经因果信息提取器用于未观察到的原因.
Keng-Hou Leong1,2, Yuxuan Xiu1,2, Bokui Chen1,3
1Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
Entropy (Basel, Switzerland)
|January 22, 2024
概括
本研究介绍了神经因果信息提取器 (NCIE),用于识别因果推理中未被观察到的原因. NCIE补充了观察到的变量,改善因果发现和时间序列预测的准确性.
科学领域:
- 计算机科学 计算机科学
- 统计 统计 统计 统计
- 机器学习 机器学习
背景情况:
- 因果推理试图准确地表示变量之间的因果关系.
- 实际系统通常涉及部分观察到的变量,其中未观察到的因素可以显著影响结果.
- 确定这些未观察到的因果因素是现场持续存在的挑战.
研究的目的:
- 开发一种方法来从未观察到的原因中提取信息,同时保留观察到的原因.
- 构建隐性变量,代表未观察到的原因的影响.
- 提供一套完整的因果因素,包括观察到的和推断到的未观察到的因素.
主要方法:
- 使用了一个称为神经因果信息提取器 (NCIE) 的生成器-区分器框架.
- 该框架旨在生成隐性变量,这些变量补充来自未观察到原因的信息.
- 相互信息最大化用于确保生成的隐性变量捕获相关的因果信息.
主要成果:
- 合成实验表明,生成的隐性变量有效地保留了未观察到原因的信息和动态.
- 当包含隐性变量时,现实世界时间序列预测任务显示出更高的精度.
- 结果表明,生成的隐性变量与目标变量具有因果相关性.
结论:
- 神经因果信息提取器 (NCIE) 成功生成了代表未观察到原因的隐性变量.
- 拟议的方法通过提供更完整的因果关系图片来增强因果推理.
- 纳入这些隐性变量可以提高复杂任务的性能,例如时间序列预测.
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