Causal discovery methods aim to infer causal direction from observational data. Functional causal discovery approaches use structural asymmetries to identify causal directionality but rely on strong modeling assumptions and provide limited tools for uncertainty quantification. We introduce Causal Discovery via Statistical Power (CDSP), a statistical inference framework that connects causal direction estimation with statistical power and enables uncertainty quantification. Considering the foundational setting of bivariate observational data, we show how quantities analogous to statistical power and effect size can be used in causal discovery to determine when data contain sufficient information to favor one direction over the other. We introduce the effect-size asymmetry assumption that characterizes when the probability of correctly detecting the causal direction (i.e., the power of causal discovery) exceeds that of incorrectly favoring the reverse direction. We show that the effect-size asymmetry assumption can be used for causal direction estimation with uncertainty quantification. Simulations show that CDSP direction estimation is robust to mild and moderate model misspecifications. Real data analyses on 100 cause-effect benchmark pairs further demonstrate that CDSP reduces false discovery rates by approximately 18% relative to a commonly used existing method.

翻译：因果发现方法旨在从观测数据中推断因果方向。功能性因果发现方法利用结构不对称性来识别因果方向性，但依赖于强建模假设，且为不确定性量化提供的工具有限。我们提出统计效力因果发现(CDSP)，这是一种将因果方向估计与统计效力关联起来的统计推理框架，能够实现不确定性量化。考虑双变量观测数据的基础设定，我们展示了如何将类似于统计效力和效应量的概念用于因果发现，以判断数据是否包含足够信息支持某一方向优于另一方向。我们引入效应量不对称假设，该假设刻画了正确检测因果方向的概率（即因果发现的效力）超过错误偏向反方向概率的条件。我们证明，效应量不对称假设可用于带有不确定性量化的因果方向估计。模拟实验表明，CDSP方向估计对轻度和中度模型误设具有稳健性。对100个因果效应基准对的真实数据分析进一步显示，CDSP相比常用现有方法将错误发现率降低了约18%。