Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets

被引：0

作者：

Kumor, Daniel ^{[1
]}

Cinelli, Carlos ^{[2
]}

Bareinboim, Elias ^{[3
]}

机构：

[1] Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA

[2] Univ Calif Los Angeles, Dept Stat, Los Angeles, CA USA

[3] Columbia Univ, Dept Comp Sci, New York, NY 10027 USA

来源：

INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 119 | 2020年 / 119卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We develop a polynomial-time algorithm for identification of structural coefficients in linear causal models that subsumes previous efficient state-of-the-art methods, unifying several disparate approaches to identification in this setting. Building on these results, we develop a procedure for identifying total causal effects in linear systems.

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页数：10

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