在因果发现后对因果效应的选择后推断
Ting-Hsuan Chang1, Zijian Guo2, Daniel Malinsky1
1Department of Biostatistics, Columbia University.
Biometrika
|January 30, 2026
概括
这项研究引入了一种用于因果发现算法的新的选择后推断方法. 它通过使用重新抽样和查,即使在模型选择后,也确保了因果关系的准确置信区间.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 因果推理因果推理
背景情况:
- 基于约束的因果发现算法使用条件独立性测试来识别图形因果模型.
- 这些模型为因果效应估计提供了信息,但在有效的推断选择后面面临挑战.
- 对于模型选择和估计的天真数据使用产生了无效的置信区间.
研究的目的:
- 开发一种因果发现的选择后推断方法,为因果效应参数提供异常正确的覆盖范围.
- 解决因果发现和估计中使用数据两次导致的无效置信区间问题.
- 为了确保推断性说法对人口层面的影响是有效的,而不是依赖数据的数量.
主要方法:
- 建议采用重新抽样和查程序,使用随机中间统计数据多次执行因果发现.
- 因果效应估计和信心集是通过将单个基于图表的结果结合起来来构建的.
- 该方法是使用PC算法用于定向环形图和多变量高斯分布来演示的.
主要成果:
- 拟议的方法实现了对真实因果效应参数的异常正确覆盖.
- 保证对固定的人口水平效应来说,置信度集的有效性是保证的.
- 该方法是通用和模块化的,适用于各种发现算法和分布式家族.
结论:
- 开发的选择后推断技术提供了一个统计学上合理的方法,用于在模型选择后估计因果效应.
- 这种方法提高了因果推理的可靠性,在存在关于模型结构的不确定性.
- 该方法的模块化允许在不同的因果发现框架中广泛适用.
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