概括
随机对照试验 (RCT) 对于因果推断本身并不"足够". 统计方法需要理解社会背景和经验数据,而不仅仅是直觉,特别是观测研究.
科学领域:
- 统计 统计 统计 统计
- 社会学 社会学 社会学
- 因果推理因果推理
背景情况:
- 阿罗诺夫和其他人. (2021) 认为随机对照试验 (RCT) 足以推断因果关系,而观察性研究的非参数方法则不足.
- 在统计充分性中",足够"的概念被认为是与同行评审等制度过程相关的社会学主张,而不是纯粹的统计.
- 现有的统计实践往往依赖于关于现实数据复杂性的直觉,特别是关于倾向得分函数.
研究的目的:
- 批判性地评估随机对照试验 (RCT) 对于因果推断来说"足够"的说法.
- 为更广泛的统计充分性概念辩论,包括对社会背景的经验知识.
- 倡导在观测研究中对因果推理的复杂性进行超科学研究.
主要方法:
- 在统计充分性中对"足够"的定义和含义进行批判性分析.
- 检查影响统计实践的社会学和制度因素.
- 应用批评推系统作为一个案例研究,突出基于直觉的方法的局限性.
主要成果:
- 统计方法的充分性不仅取决于统计逻辑,还取决于它们与上下文知识的整合.
- 完全理解"足够"需要知识超越倾向分数,包括各种空间和时间的社会特征.
- 统计学批评的实际实用性在很大程度上取决于具体的应用上下文,例如推系统.
结论:
- 统计学家应该超越直觉,进行实证的超科学,以了解观察数据中因果推理的复杂性.
- 在统计学中依赖"免费午餐",通常仅限于科技公司的利应用,应该受到质疑.
- 实践统计学家必须明确考虑"为什么?" 和"为了谁?" 问题,以适当地解决他们的方法的充分性.
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