同变量辅助的因果效应与仪表变量之间的因果界限
Alexander W Levis1, Matteo Bonvini1, Zhenghao Zeng1
1Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Journal of the Royal Statistical Society. Series B, Statistical methodology
|November 13, 2025
概括
仪表变量 (IVs) 帮助估计因果效应,当数据未测量时. 新方法在观察和试验环境中为平均治疗效应 (ATE) 提供了更严格的界限,即使数据复杂.
科学领域:
- 因果推理的原因推理.
- 计量经济学 计量经济学
- 生物统计学 生物统计学
背景情况:
- 仪表变量 (IV) 在存在未测量的混时,对于估计因果关系至关重要.
- 传统的IV方法通常需要强有力的,无法测试的假设来确定平均治疗效应 (ATE).
- 现有的ATE边界是有价值的,但在复杂的观测和试验环境中存在局限性.
研究的目的:
- 扩大仪器变量边界对平均治疗效果 (ATE) 在使用基线混器的观察性研究中的实用性.
- 适应随机试验的IV界限,包括测量的基线共变量.
- 开发新的统计方法,以高效地估计这些边界.
主要方法:
- 证明了Balke和Pearl的严格界限在使用IV混剂的观察环境和使用共变量的随机试验中的适用性.
- 建议在新保证金条件下对基于函数的估计器产生影响,以实现参数收率.
- 开发了用于平滑近似非平滑ATE边界的估计器,并扩展了连续结果的方法.
主要成果:
- 拟议的方法在更复杂的现实场景中为平均治疗效果 (ATE) 提供了可靠的边界.
- 基于影响函数的估计器证明了通过灵活的干扰函数建模来实现参数收率的潜力.
- 模拟探索了有限样本属性,并将其应用于高等教育对工资的影响,说明了实际效用.
结论:
- 该研究成功地将仪器变量界限技术扩展到更广泛的观测和试验数据设置.
- 新的估计策略为因果推理中不平滑的功能界限提供了更高的效率.
- 这些发现为估计未测量的混杂和共变量存在的因果关系提供了有价值的工具.
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