Problem
Research questions and friction points this paper is trying to address.
causal discovery
observational data
conditional independence tests
structure scores
functional assumptions
Decoupled Causal Discovery
🤖 AI Summary
本文提出了一种基于解耦的因果发现方法DCD，通过加权函数解耦非目标变量直接识别马尔可夫边界，并迭代构建CPDAG，以解决观测数据中因果关系发现的问题。
📝 Abstract
Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on conditional independence tests, structure scores, or restrictive functional assumptions, we propose Decoupled Causal Discovery (DCD), a novel decoupling-based perspective that does not rely on these methodologies. DCD directly identifies the Markov boundary (MB) by decoupling non-target variables via weighting functions, such that only variables within the MB preserve dependence with the target under the decoupled distribution. Building on this, DCD iteratively constructs the Completed Partially Directed Acyclic Graph (CPDAG) by exploiting structural asymmetries within the MBs. We establish the theoretical identifiability, soundness, and completeness of DCD. Empirical evaluations demonstrate that DCD achieves strong performance, particularly excelling in challenging noise regimes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decoupled Causal Discovery
Markov boundary
weighting functions
Completed Partially Directed Acyclic Graph
structural asymmetries
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