基于约束的因果发现的条件独立性检验：综述
Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey
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中文总结 AI 辅助
该综述针对生物医学领域常见的高维混合类型场景，梳理六大类条件独立性检验方法，分析其优缺点，关联测试级与图级错误，对比主流库应用并总结开放挑战。
中文摘要 AI 辅助
条件独立性（CI）检验是基于约束的因果发现的统计引擎：在PC（Peter-Clark）和FCI（快速因果推断）等算法中，骨架剪枝和关键定向直接源于CI决策。本综述回顾了CI检验，重点关注生物医学领域常见的高维和混合类型设置下的假设、稳健性和可扩展性。综述将广泛使用的CI方法分为六大类：偏相关、列联表、回归、近邻、核和基于机器学习的方法。特别强调了解决这些类别的局限性的稳健性层。对于每个类别，综述考察了CI决策何时反映数据生成分布、何时失效。通过这种方式，我们将测试级属性（包括随着条件集大小增加而下降的功效，以及I/II型错误的不对称后果）与骨架恢复和v结构定向中的图级错误联系起来。综述还比较了其在主要R和Python库中的应用情况，并总结了开放挑战，包括无需离散化的混合类型CI检验、小样本错误控制，以及提高CI检验可扩展性的策略。
英文摘要
Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleton pruning and key orientations follow directly from CI decisions. This survey reviews CI testing with emphasis on assumptions, robustness, and scalability in high-dimensional and mixed-type settings common in biomedical domains. The survey organizes widely used CI methods into six families: partial-correlation, contingency-table, regression, nearest-neighbor, kernel, and machine-learning-based. Special emphasis is provided on the robustness layers that address the limitations of these families. For each family, the survey examines when CI decisions reflect the data-generating distribution and when they fail. By this, we link test-level properties, including power decay with conditioning set size and asymmetric type I/II error consequences, to graph-level errors in skeleton recovery and v-structure orientation. The survey also compares adoption across major R and Python libraries and summarizes open challenges, including mixed-type CI testing without discretization, small-sample error control, and strategies for improving scalability of CI-testing.
发表机构
- University of Nicosia（尼科西亚大学）
- School of Sciences and Engineering（科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
