条件和平均治疗效果的边界与未观察到的混因素
Steve Yadlowsky1, Hongseok Namkoong2, Sanjay Basu3
1Google Research, Brain Team.
概括
这项研究引入了一种新方法,用于在观察性研究中估计因果关系,即使不测量混. 该方法为治疗效应提供了可靠的边界,提高了因果推理准确度.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 机器学习 机器学习
背景情况:
- 观察性研究容易因影响治疗分配的未观察到的混因素而产生偏差.
- 准确估计因果关系效应需要解决潜在的未测量的混.
- 现有的方法在处理复杂数据结构时可能缺乏可扩展性或灵活性.
研究的目的:
- 开发一种可靠的方法,用于在未观察到的混下进行因果推理的灵敏性分析.
- 估计条件平均治疗效应 (CATE) 和平均治疗效应 (ATE) 的边界.
- 提出一种可扩展和灵活的方法,利用机器学习方法.
主要方法:
- 一个损失最小化框架,用于在CATE上估计边界,使用边界未观察到的混因子.
- 在未观察到的混杂下,扩展ATE的增强逆倾向加权 (AIPW) 估计器.
- 使用尼曼直角分数来进行ATE边界的正则根-n估计.
- 整合非参数和黑盒机器学习模型.
主要成果:
- 拟议的方法为CATE和ATE提供了可扩展和灵活的边界估计.
- 在特定条件下,ATE边界的半参数估计器是常规的n根估计器.
- 优化结果表明,在某些场景中,拟议的界限很紧.
- 在模拟和真实数据上的实证验证显示了准确的置信区间覆盖范围.
结论:
- 开发的方法提供了一种基于原则的方法,用于因果推断的灵敏度分析.
- 该方法适用于复杂的数据集和各种机器学习模型.
- 这项工作提升了从观测数据中得出可靠的因果结论的能力,尽管没有测量的混.
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