用于因果推断的解混：关于麦卡弗里和丹克斯的思考
Unmixing for Causal Inference: Thoughts on McCaffrey and Danks.
作者信息
Zhang Kun, Glymour Madelyn R K
机构信息
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA,
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA.
出版信息
Br J Philos Sci. 2018 Aug 10;71(4):1319-1330. doi: 10.1093/bjps/axy040. eCollection 2020 Dec.
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. 1Introduction2Background3Addressing the Problem4Discussion.
麦卡弗里和丹克斯提出，在功能磁共振成像（fMRI）中，从混合分布数据中发现因果关系是不可能的。我们给出了一种算法，该算法可解决fMRI研究中通常假定的分布的这一问题，并发现，在测试中，它能够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，混合分布问题并非无法解决，而是一项挑战。1引言2背景3解决问题4讨论
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