论文标题

当控制变量未知时,因果效应的推断

Inference of Causal Effects when Control Variables are Unknown

论文作者

Hult, Ludvig, Zachariah, Dave

论文摘要

因果效应中的常规方法推断性地依赖于指定一组有效的控制变量。当此集合未知或误指定时,推论将是错误的。当观察到所有潜在的混杂因素时,我们提出了一种推断平均因果效应的方法,但是尚不清楚控制变量。当数据生成过程属于关联线性结构因果模型的类别时,我们证明theShod会产生渐近有效的置信区间。我们的结果基于线性定向无环图的平滑表征。我们验证该方法使用合成数据产生有效置信区间的有效置信区间的能力,即使对控制变量的适当规范是未知的。

Conventional methods in causal effect inferencetypically rely on specifying a valid set of control variables. When this set is unknown or misspecified, inferences will be erroneous. We propose a method for inferring average causal effects when all potential confounders are observed, but thecontrol variables are unknown. When the data-generating process belongs to the class of acyclical linear structural causal models, we prove that themethod yields asymptotically valid confidence intervals. Our results build upon a smooth characterization of linear directed acyclic graphs. We verify the capability of the method to produce valid confidence intervals for average causal effects using synthetic data, even when the appropriate specification of control variables is unknown.

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