Unmixing for Causal Inference: Thoughts on McCaffrey and Danks.
Unmixing for Causal Inference: Thoughts on McCaffrey and Danks.
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因果推理的分解：对麦卡弗里和丹克斯的思考。
DOI：
10.1093/bjps/axy040
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Glymour,MadelynRK
中科院分区：
文献类型：
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作者：
Zhang,Kun;Glymour,MadelynRK
McCaffrey和Danks提出了从混合分布数据中发现因果关系的挑战，作为功能磁共振（fMRI）的不可能结果。我们给出了一种算法来解决fMRI研究中通常假设的分布的这个问题，并在测试中发现，它可以准确地从混合分布中分离数据。与自动搜索的其他障碍一样，混合分布的问题不是不可能解决的问题，而是一个挑战。
McCaffrey and Danks have posed the challenge of discovering causal relations in data drawn from a mixture of distributions as an impossibility result in functional magnetic resonance (fMRI). We give an algorithm that addresses this problem for the distributions commonly assumed in fMRI studies and find that in testing, it can accurately separate data from mixed distributions. As with other obstacles to automated search, the problem of mixed distributions is not an impossible one, but rather a challenge.