论文标题
因果发现后有效的推断
Valid Inference After Causal Discovery
论文作者
论文摘要
因果发现和因果效应估计是因果推断的两个基本任务。尽管为每个任务开发了许多方法，但共同应用这些方法时会出现统计挑战：在运行因果发现算法对相同数据的因果关系效果估算后，导致“双重浸入”，使经典置信区间的覆盖范围保证无效。为此，我们开发了有效的可杀后发现推断的工具。在经验研究中，我们表明，因果发现和随后的推论算法的幼稚组合导致高度膨胀的误解率。另一方面，应用我们的方法提供了可靠的覆盖范围，同时获得比数据分裂更准确的因果发现。
Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to "double dipping," invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. Across empirical studies, we show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates; on the other hand, applying our method provides reliable coverage while achieving more accurate causal discovery than data splitting.
