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，仍可通过以下方式利用观测数据缩小平均处理效应（ATE）的可能取值范围：（1）将因果图识别至马尔可夫等价类；（2）估计该类中每个图对应的ATE。虽然PC算法能在强忠实性假设下识别等价类，但其计算量可能过大。幸运的是，识别ATE可能取值集合仅需处理变量周围的局部图结构——局部发现算法正是利用这一事实来提升计算效率。本文提出基于急切碰撞检测的局部发现算法（LDECC），这是一种新的局部因果发现算法，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，其中LDECC的计算效率指数级优于现有局部发现算法，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，我们利用这一见解削弱了识别ATE可能取值集合所需的假设条件。