Measurement Error and Causal Discovery
Measurement Error and Causal Discovery
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发表时间：
2016-06
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通讯作者：
R. Scheines;J. Ramsey
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作者：
R. Scheines;J. Ramsey
因果发现算法出现于20世纪90年代初，并自那时起迅速发展[4,10]。在因果结构（因果图）的有向无环图形表示与条件独立关系（因果马尔可夫条件1和分离2）联系起来之后，因果图（模式）的马尔可夫等价类的图形表征很快就出现了，同时还有点一致算法来搜索模式。哲学、统计学和计算机科学领域的研究人员已经产生了基于约束的算法、基于分数的算法、信息论算法、非高斯误差线性模型的算法、涉及因果反馈的系统的算法、包含不可测量的共同原因的等价类的算法、时间序列的算法、处理实验和非实验数据的算法。处理在变量的适当子集上重叠的数据集的算法，以及发现涉及数十个“指标”的心理测量模型的测量模型结构的算法。在许多情况下，我们已经证明了这些算法的渐近可靠性，并且在几乎所有情况下，我们都进行了模拟研究，使我们对这些算法的有限样本精度有所了解。我们在这里介绍的FGES算法（快速贪婪等价搜索，[6]）在各种情况下都非常准确，并且在稀疏图的一百万个变量上计算易于处理。许多算法已经被应用于严肃的科学问题，比如从功能磁共振成像（fMRI）数据中区分自闭症和神经正常的受试者，人们对这一领域的兴趣似乎正在激增。
Algorithms for causal discovery emerged in the early 1990s and have since proliferated [4, 10]. After directed acyclic graphical representations of causal structures (causal graphs) were connected to conditional independence relations (the Causal Markov Condition1 and dseparation2), graphical characterizations of Markov equivalence classes of causal graphs (patterns) soon followed, along with pointwise consistent algorithms to search for patterns. Researchers in Philosophy, Statistics, and Computer Science have produced constraint-based algorithms, score-based algorithms, information-theoretic algorithms, algorithms for linear models with non-Gaussian errors, algorithms for systems that involve causal feedback, algorithms for equivalence classes that contain unmeasured common causes, algorithms for time-series, algorithms for handling both experimental and non-experimental data, algorithms for dealing with datasets that overlap on a proper subset of their variables, and algorithms for discovering the measurement model structure for psychometric models involving dozens of “indicators”. In many cases we have proofs of the asymptotic reliability of these algorithms, and in almost all cases we have simulation studies that give us some sense of the finite-sample accuracy of these algorithms. The FGES algorithm (Fast Greedy Equivalence Search, [6]), which we feature here, is highly accurate in a wide variety of circumstances and is computationally tractable on a million variables for sparse graphs. Many algorithms have been applied to serious scientific problems like distinguishing between Autistic and neurotypical subjects from fMRI data [2], and interest in the field seems to be exploding.