Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class. While the PC algorithm can identify this class under strong faithfulness assumptions, it can be computationally prohibitive. Fortunately, only the local graph structure around the treatment is required to identify the set of possible ATE values, a fact exploited by local discovery algorithms to improve computational efficiency. In this paper, we introduce Local Discovery using Eager Collider Checks (LDECC), a new local causal discovery algorithm that leverages unshielded colliders to orient the treatment's parents differently from existing methods. We show that there exist graphs where LDECC exponentially outperforms existing local discovery algorithms and vice versa. Moreover, we show that LDECC and existing algorithms rely on different faithfulness assumptions, leveraging this insight to weaken the assumptions for identifying the set of possible ATE values.

翻译：即使我们不知道数据背后的因果图，也可以通过观测数据来缩小平均处理效应（ATE）可能取值的范围，方法包括：（1）识别出马尔可夫等价类的图结构；（2）估计该类中每个图对应的ATE。尽管PC算法能在强忠实性假设下识别此类图结构，但其计算成本可能过高。幸运的是，仅需处理变量周围的局部图结构即可识别可能的ATE取值集合——这一特性被局部发现算法利用以提高计算效率。本文提出一种新的局部因果发现算法——利用急迫碰撞检验的局部发现（LDECC），该算法通过利用非屏蔽碰撞结构以不同于现有方法的方式定向处理变量的父节点。我们证明存在某些图结构使得LDECC比现有局部发现算法呈指数级性能提升，反之亦然。此外，研究表明LDECC与现有算法依赖不同的忠实性假设，利用这一洞察可弱化识别潜在ATE取值集合所需的前提条件。