发表机构
Georgia Tech; TU Munich; Rational Intelligence; Helmholtz Munich; CISPA（佐治亚理工学院; 慕尼黑工业大学; Rational Intelligence; 亥姆霍兹慕尼黑中心; CISPA亥姆霍兹信息安全中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出利用数据对称性作为新的约束来源，通过形状约束和测度变换两种方式收紧因果效应的部分识别界限，并在理论和实验中验证其有效性。
AI 中文摘要
部分识别（PI）通过将关于数据生成过程的不同假设编码为约束优化问题来估计因果效应的界限。即使因果效应本身不可识别，这些界限也可能足以指导政策决策。然而，在实践中这些界限常常是空洞的，研究者们试图详尽地编码领域知识作为额外约束，以使PI界限更具信息量。我们引入了已知的数据对称性——即因果效应在特定数据变换下的不变性——作为约束的新来源，以指导PI。我们将其操作化为对因果函数的形状约束，并通过一种测度变换，利用简单的数据预处理来构建PI。两种方法均被证明能在两个经典PI模型下收紧界限。这一结论在总体情形下通过理论分析得到验证，并在有限样本情形下通过实验得到证实。更广泛地，我们的框架确立了数据对称性作为一种自然的、未被充分利用的背景知识来源，用于稳健的因果推断。
英文摘要
Partial identification (PI) entails estimating bounds on causal effects by encoding different assumptions on data generation as a constrained optimization problem. Such bounds can suffice to inform policy decisions even if the causal effect itself is not identifiable. Often vacuous in practice, practitioners seek to exhaustively encode domain knowledge as additional constraints to make the PI bounds more informative. We introduce known data symmetries -- invariance of the causal effect under certain data transformations -- as a new source of constraints to inform PI. We operationalize this as a shape constraint on the causal function, and via a change of measure against which PI is posed using simple data pre-processing. Both approaches are shown to sharpen bounds under two canonical PI models. This is shown both theoretically for the population case, and via experiments in the finite-sample case. More broadly, our framework establishes data symmetries as a natural, underutilized source of background knowledge for robust causal inference.
