论文标题
使用反事实之间的逻辑关系得出界限和不平等约束
Deriving Bounds and Inequality Constraints Using LogicalRelations Among Counterfactuals
论文作者
论文摘要
在未观察到的混杂存在下，可能无法确定因果参数。但是，在某些情况下，仍可以从观察到的数据中恢复有关非识别参数的信息。我们开发了一种新的常规方法，使用概率规则和因果图形模型暗示的反事实限制，以获取因果参数的界限。我们还对此类因果模型隐含的观察到的数据定律的功能提供了不平等限制。我们的方法是通过观察到的，即确定的和未识别的反事实事件之间的逻辑关系通常会产生有关非识别事件的信息。我们表明，这种方法足够强大，可以恢复已知的尖锐界限和严格的不平等约束，并得出新颖的界限和约束。
Causal parameters may not be point identified in the presence of unobserved confounding. However, information about non-identified parameters, in the form of bounds, may still be recovered from the observed data in some cases. We develop a new general method for obtaining bounds on causal parameters using rules of probability and restrictions on counterfactuals implied by causal graphical models. We additionally provide inequality constraints on functionals of the observed data law implied by such causal models. Our approach is motivated by the observation that logical relations between identified and non-identified counterfactual events often yield information about non-identified events. We show that this approach is powerful enough to recover known sharp bounds and tight inequality constraints, and to derive novel bounds and constraints.
