提出首个可保证识别的潜变量因果结构贪心搜索方法
Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
- 基于评分的贪心搜索，引入广义因子模型解决潜变量问题
- 理论证明真实结构可在马尔可夫等价类内被准确识别
- 算法高效搜索图空间，适合含潜变量的真实数据场景
识别部分可观测因果系统结构对多个科学领域至关重要。近年来研究多聚焦于基于约束的因果发现，但实际中常面临多重检验与误差传播问题。这些问题可通过基于评分的方法缓解，因此亟需一种能在部分可观测情形下工作的评分贪心搜索方法。本文首次提出适用于含潜变量结构识别的评分贪心搜索方法，并构建广义N因子模型，建立全局一致性：通过评分可将真实结构（含潜变量）唯一识别至马尔可夫等价类。进而设计潜变量贪心等价搜索（LGES）算法，具备明确算子，可在图空间高效搜索最优结构。在合成与真实数据上的实验验证了方法有效性（代码将公开）。
原文摘要 · Abstract (English)
Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constraint-based causal discovery to solve this problem, and yet in practice these methods often face challenges related to multiple testing and error propagation. These issues could be mitigated by a score-based method and thus it has raised great attention whether there exists a score-based greedy search method that can handle the partially observed scenario. In this work, we propose the first score-based greedy search method for the identification of structure involving latent variables with identifiability guarantees. Specifically, we propose Generalized N Factor Model and establish the global consistency: the true structure including latent variables can be identified up to the Markov equivalence class by using score. We then design Latent variable Greedy Equivalence Search (LGES), a greedy search algorithm for this class of model with well-defined operators, which search very efficiently over the graph space to find the optimal structure. Our experiments on both synthetic and real-life data validate the effectiveness of our method (code will be publicly available).
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