On the Sample Complexity of Causal Discovery and the Value of Domain Expertise
On the Sample Complexity of Causal Discovery and the Value of Domain Expertise
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论因果发现的样本复杂性和领域专业知识的价值
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Roy Dong
中科院分区：
文献类型：
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作者：
Samir Wadhwa;Roy Dong
因果发现方法寻求从纯粹的观测数据中识别随机变量之间的因果关系，而不是主动收集的实验数据，其中实验者干预相关的子集。这一领域的开创性工作之一是推断因果算法，它保证了在条件独立(CI)预言的假设下成功的因果发现：一种预言，它可以声明两个随机变量是否在给定另一组随机变量的情况下条件独立。该算法的实际实现包含了对条件独立性的统计测试，而不是CI预言。在本文中，我们分析了没有CI预言的因果发现算法的样本复杂性：给定一定的置信度，因果发现算法需要多少个数据点来识别因果结构？此外，我们的方法允许我们根据数据样本量化领域专业知识的价值。最后，我们用数值例子证明了这些采样率的准确性，并量化了稀疏性先验和已知因果方向的好处。
Causal discovery methods seek to identify causal relations between random variables from purely observational data, as opposed to actively collected experimental data where an experimenter intervenes on a subset of correlates. One of the seminal works in this area is the Inferred Causation algorithm, which guarantees successful causal discovery under the assumption of a conditional independence (CI) oracle: an oracle that can states whether two random variables are conditionally independent given another set of random variables. Practical implementations of this algorithm incorporate statistical tests for conditional independence, in place of a CI oracle. In this paper, we analyze the sample complexity of causal discovery algorithms without a CI oracle: given a certain level of confidence, how many data points are needed for a causal discovery algorithm to identify a causal structure? Furthermore, our methods allow us to quantify the value of domain expertise in terms of data samples. Finally, we demonstrate the accuracy of these sample rates with numerical examples, and quantify the benefits of sparsity priors and known causal directions.
使用辅助割集有效识别线性结构因果模型
DOI：
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发表时间：
2020
期刊：
JMLR workshop and conference proceedings
影响因子：
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作者：
Kumor, Daniel;Cinelli, Carlos;Bareinboim, Elias
通讯作者：
Bareinboim, Elias
通过干预学习和测试因果模型
DOI：
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发表时间：
2018
期刊：
32nd Annual Conference on Neural Information Processing Systems
影响因子：
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作者：
Acharya, Jayadev;Bhattacharyya, Arnab;Daskalakis, Constantinos;Kandasamy, Saravanan
通讯作者：
Kandasamy, Saravanan