Causal Inference by Surrogate Experiments: z-Identifiability
Causal Inference by Surrogate Experiments: z-Identifiability
复制标题
通过替代实验进行因果推断：z-可识别性
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
J. Pearl
中科院分区：
文献类型：
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作者：
E. Bareinboim;J. Pearl
我们解决的问题是估计干预对一组变量X的影响，而干预对另一组变量Z的实验的影响更容易被操纵。这个问题，我们称之为Z -可辨识性，当Z =空时，可以简化为普通可辨识性，并且与后者一样，可以使用do-calculus给出语法表征[Pearl, 1995；2000]。我们给出了任意集合X，Z， Y（结果）的Z -可辨识性的一个图形化的充分必要条件。我们进一步开发了一个完整的算法来计算X对Y的因果效应，利用z上的实验提供的信息。最后，我们用我们的结果证明了相对于z可识别的do-calculus的完备性，这一结果并不遵循相对于普通可识别的完备性。
We address the problem of estimating the effect of intervening on a set of variables X from experiments on a different set, Z, that is more accessible to manipulation. This problem, which we call z-identifiability, reduces to ordinary identifiability when Z = empty and, like the latter, can be given syntactic characterization using the do-calculus [Pearl, 1995; 2000]. We provide a graphical necessary and sufficient condition for z-identifiability for arbitrary sets X,Z, and Y (the outcomes). We further develop a complete algorithm for computing the causal effect of X on Y using information provided by experiments on Z. Finally, we use our results to prove completeness of do-calculus relative to z-identifiability, a result that does not follow from completeness relative to ordinary identifiability.