从局部因果推断到集群推理，解决隐藏变量下的因果发现难题。
From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
- 基于局部因果模式自动学习集群结构，无需预先指定
- 每个集群可由最多三个微观变量表示，保持宏观可识别性
- 适用于隐藏变量普遍存在的实际场景，适合因果推断研究者
隐藏变量是因果发现与推断的根本障碍。局部方法仅能捕捉邻近依赖，而集群级方法通常需提前给定集群划分或满足因果充分性，这在现实中很少成立。在集群分辨率下进行单变量发现会违反这些条件，导致系统性偏差。L2C（局部到集群因果抽象）从局部因果模式中自动学习集群结构，无需人工变量-集群映射。该框架基于集群简化定理：每个集群可由至多三个微观变量表征，且不损失宏观层面的可识别性。在存在隐藏变量的情况下，对最大祖先图进行局部发现，可恢复直接原因、结果和V结构，再通过对应演算进行集群级推断。宏观因果效应被定义为对目标集群内微观变量的联合干预。该框架具有完备性、原子完整性及多项式时间复杂度。L2C在集群未知且隐藏变量不可避免的设定下，建立了从微观局部信息到宏观因果推理的直接桥梁。
原文摘要 · Abstract (English)
Latent variables pose a fundamental obstacle to both causal discovery and inference. Local approaches exploiting direct neighborhood relations provide little beyond immediate dependencies. Cluster-level methods, though capable of broader reasoning, generally require cluster assignments or causal sufficiency in advance, conditions that are rarely satisfied in practice. Applying single-variable discovery at cluster resolution violates these conditions and thereby produces systematic bias. L2C (Local to Cluster Causal Abstraction) learns cluster structure from local causal patterns without manual variable-to-cluster mapping. The framework rests on a cluster reduction theorem: each cluster can be represented by at most three micro-variables, with no loss of macro-level identifiability. In the presence of latent variables, local discovery on maximal ancestral graphs recovers direct causes, effects, and V-structures, followed by cluster-level inference through a corresponding calculus on the resulting graph. Macro causal effects are formulated as joint interventions on the micro-variables within each target cluster. The framework is sound, atomically complete, and polynomially bounded. L2C establishes a direct connection from local micro-level information to macro causal reasoning in settings where clusters are unknown and latent variables are unavoidable.
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